阅读说明：本技术 功率逐步唤醒机制 (Power step-by-step wake-up mechanism ) 是由 伊扎克·阿布迪 梅厄·阿加西 阿里耶·莱纳 内森·阿尔特曼 桑德普·狄索莎 于 2016-09-09 设计创作，主要内容包括：公开功率逐步唤醒机制的设备和方法。在一个实施例中,基于指纹图像的检测而启动装置的方法可包含：监测所述指纹图像的第一组区域的第一度量水平；响应于所述第一度量水平超过第一阈值而确定所述指纹图像的第二组区域的第二度量水平；以及基于所述指纹图像的所述第二组区域的所述第二度量水平而启动所述装置。(Apparatus and methods of a power gradual wake-up mechanism are disclosed. In one embodiment, a method of activating a device based on detection of a fingerprint image may include: monitoring a first metric level for a first set of regions of the fingerprint image; determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold; and activating the device based on the second metric levels for the second set of regions of the fingerprint image.)

1. A method for activating a device based on detection of a fingerprint image, the method comprising:

monitoring a first metric level for a first set of regions of a fingerprint image;

determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, wherein the second set of regions includes a different extent than the first set of regions; and

activating the device based on the second metric levels for the second set of regions of the fingerprint image.

2. The method of claim 1, wherein the first metric level and the second metric level represent at least one of an acoustic energy level, an acoustic loading level, a spatial frequency, a cross-correlation value, or an image quality value.

3. The method of claim 1, wherein monitoring the first metric level for the first set of regions of the fingerprint image comprises:

receiving first sample data from the first set of regions of the fingerprint image at a first sampling rate; and

determining the first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data.

4. The method of claim 1 or 3, further comprising:

continuing to monitor the first metric level for the first set of regions of the fingerprint image in response to the first metric level being less than or equal to the first threshold.

5. The method of claim 1, wherein determining the second metric level for the second set of regions of the fingerprint image comprises:

receiving second sample data from the second set of regions of the fingerprint image; and

determining the second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data.

6. The method of claim 1, wherein activating the device based on the second metric levels for the second set of regions of the fingerprint image comprises:

determining the presence of a finger in response to the second metric exceeding a second threshold; and

activating the device in response to the presence of the finger.

7. The method of claim 6, further comprising:

monitoring the first metric for the first set of regions of the fingerprint image in response to the second metric being less than or equal to the second threshold.

8. The method of claim 3, wherein determining the first metric level for the first set of regions of the fingerprint image comprises:

determining a foreground variation based on a presence of the fingerprint image;

performing a background estimation of the first set of regions of the fingerprint image; and

determining the first metric level for the first set of regions based on a difference between the foreground variation and the background estimate for the first set of regions of the fingerprint image.

9. The method of claim 8, wherein determining the foreground variation comprises:

receiving first sampled foreground data, wherein the first sampled foreground data is collected with an ultrasonic transmitter in an enabled state;

receiving second sampled foreground data, wherein the second sampled foreground data is collected with an ultrasonic transmitter in a disabled state; and

calculating the foreground variation of the first set of regions of the fingerprint image as a difference between the first sampled foreground data and the second sampled foreground data.

10. The method of claim 8, wherein performing the context estimation comprises:

determining an updated acquisition time delay and an updated ultrasonic transmitter frequency from a change between a current temperature and a reference temperature, the initial background estimate and the initial ultrasonic transmitter frequency being determined from the reference temperature;

acquiring background image information based on the updated acquisition time delay and the updated ultrasound transmitter frequency; and

calculating the background estimate using the background image information.

11. The method of claim 10, further comprising at least one of:

reducing background noise based on an autocorrelation of the pixels in the first set of regions;

reducing sensor artifacts by removing static values in the first sampled data;

or a combination thereof.

12. An apparatus, comprising:

a sensor having a plurality of sensor pixels configured to sense a fingerprint image;

a memory configured to store the fingerprint image; and

a controller coupled to the sensor and the memory and configured to:

monitoring a first metric level for a first set of regions of the fingerprint image;

determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, wherein the second set of regions includes a different extent than the first set of regions; and

activating the device based on the second metric levels for the second set of regions of the fingerprint image.

13. The apparatus of claim 12, wherein the first metric level and the second metric level represent at least one of an acoustic energy level, an acoustic loading level, a spatial frequency, a cross-correlation value, or an image quality value.

14. The device of claim 12, wherein the controller is further configured to:

receiving first sample data from the first set of regions of the fingerprint image at a first sampling rate; and

determining the first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data.

15. The device of claim 12 or 14, wherein the controller is further configured to:

continuing to monitor the first metric level for the first set of regions of the fingerprint image in response to the first metric level being less than or equal to the first threshold.

16. The device of claim 12, wherein the controller is further configured to:

receiving second sample data from the second set of regions of the fingerprint image; and

determining the second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data.

17. The device of claim 12, wherein the controller is further configured to:

determining the presence of a finger in response to the second metric exceeding a second threshold; and

activating the device in response to the presence of the finger.

18. The device of claim 17, wherein the controller is further configured to:

monitoring the first metric for the first set of regions of the fingerprint image in response to the second metric being less than or equal to the second threshold.

19. The device of claim 14, wherein the controller is further configured to:

determining a foreground variation based on a presence of the fingerprint image;

performing a background estimation of the first set of regions of the fingerprint image; and

determining the first metric level for the first set of regions based on a difference between the foreground variation and the background estimate for the first set of regions of the fingerprint image.

20. The device of claim 19, wherein the controller is further configured to:

receiving first sampled foreground data, wherein the first sampled foreground data is collected with an ultrasonic transmitter in an enabled state;

receiving second sampled foreground data, wherein the second sampled foreground data is collected with an ultrasonic transmitter in a disabled state; and

calculating the foreground variation of the first set of regions of the fingerprint image as a difference between the first sampled foreground data and the second sampled foreground data.

21. The device of claim 19, wherein the controller is further configured to:

determining an updated acquisition time delay and an updated ultrasonic transmitter frequency from a change between a current temperature and a reference temperature, the initial background estimate and the initial ultrasonic transmitter frequency being determined from the reference temperature;

acquiring background image information based on the updated acquisition time delay and the updated ultrasound transmitter frequency; and

calculating the background estimate using the background image information.

22. The device of claim 21, wherein the controller is further configured to perform at least one of:

reducing background noise based on an autocorrelation of the pixels in the first set of regions;

reducing sensor artifacts by removing static values in the first sampled data;

or a combination thereof.

23. A non-transitory medium storing instructions for execution by one or more processors of a device, the instructions comprising:

instructions for monitoring a first metric level for a first set of regions of a fingerprint image;

instructions for determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, wherein the second set of regions includes a different extent than the first set of regions; and

instructions for activating the device based on the second metric levels for the second set of regions of the fingerprint image.

24. The non-transitory medium of claim 23, wherein the instructions for monitoring the first metric level for the first set of regions of the fingerprint image comprise:

instructions for receiving first sample data from the first set of regions of the fingerprint image at a first sampling rate; and

instructions for determining the first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data.

25. The non-transitory medium of claim 23, wherein the instructions for determining the second metric level for the second set of regions of the fingerprint image comprise:

instructions for receiving second sampled data from the second set of regions of the fingerprint image; and

instructions for determining the second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data.

26. The non-transitory medium of claim 23, wherein the instructions for activating the device based on the second metric levels of the second set of regions of the fingerprint image comprise:

instructions for determining a presence of a finger in response to the second metric level exceeding a second threshold; and

instructions for activating the device in response to the presence of the finger.

27. The non-transitory medium of claim 24, wherein the instructions for determining the first metric level for the first set of regions of the fingerprint image comprise:

instructions for determining a foreground variation based on a presence of the fingerprint image;

instructions for performing a background estimation of the first set of regions of the fingerprint image; and

instructions for determining the first metric level for the first set of regions of the fingerprint image based on a difference between the foreground variation and the background estimate for the first set of regions.

28. An apparatus, comprising:

means for sensing a fingerprint image using a plurality of sensor pixels;

means for storing the fingerprint image;

means for monitoring a first metric level for a first set of regions of the fingerprint image;

means for determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, wherein the second set of regions includes a different extent than the first set of regions; and

means for activating the device based on the second metric levels for the second set of regions of the fingerprint image.

29. The device of claim 28, wherein the means for monitoring the first metric level for the first set of regions of the fingerprint image comprises:

means for receiving first sample data from the first set of regions of the fingerprint image at a first sample rate; and

means for determining the first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data.

30. The device of claim 28, wherein the means for determining the second metric level for the second set of regions of the fingerprint image comprises:

means for receiving second sample data from the second set of regions of the fingerprint image; and

means for determining the second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data.

Technical Field

The present invention relates to the field of wireless communications. In particular, the present invention relates to wake-up mechanisms for mobile devices.

Background

Conventional mobile devices may not be able to detect whether the device is available for use in the near future until the user presses an "on/off" button or touches a portion of the display. When in this uncertain state, conventional mobile devices may remain active or may become active periodically to perform several background tasks and data synchronization when the mobile device is predicted to be available for use. Such background tasks and data synchronization may unnecessarily consume limited battery resources and/or consume communication/processing bandwidth. Thus, it would be beneficial to use a wake-up mechanism that can conserve limited battery resources, conserve communication/processing bandwidth, or both, and/or improve the operation of the mobile device by some other means.

Disclosure of Invention

The invention relates to a device and a method for a power gradual wake-up mechanism. In one embodiment, a method of activating a device based on detection of a fingerprint image may include: monitoring a first metric level for a first set of regions of a fingerprint image; determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold; and activating the device based on the second metric level for the second set of regions of the fingerprint image. The first metric level and the second metric level may represent at least one of an acoustic energy level, an acoustic loading level, a spatial frequency, a cross-correlation value, or an image quality value.

According to aspects of the present invention, a method of monitoring a first metric level for a first set of regions of a fingerprint image may comprise: first sample data is received from a first set of regions of a fingerprint image at a first sample rate, and a first metric level indicative of an initial prediction as to whether a finger is present is determined using the first sample data. The method may further include monitoring a first metric level for a first set of regions of the fingerprint image in response to the first metric level being less than or equal to a first threshold.

The method of determining a second metric level for a second set of regions of the fingerprint image may include: second sampled data is received from a second set of regions of the fingerprint image and second metric levels indicative of finer predictions as to whether a finger is present are determined using the second sampled data. In some implementations, the second set of regions can include a portion of the active area of the ultrasonic sensor or the entire active area of the ultrasonic sensor.

A method of activating a device based on a second metric level for a second set of regions of a fingerprint image may include: the presence of a finger is determined in response to the second metric exceeding a second threshold, and the device is activated in response to the presence of the finger. The method may further include monitoring a first metric level for a first set of regions of the fingerprint image in response to the second metric level being less than or equal to a second threshold.

A method of determining a first metric level for a first set of regions of a fingerprint image may include: determining foreground variation based on the presence of the fingerprint image; performing a background estimation of a first set of regions of the fingerprint image; and determining a first metric level for a first set of regions of the fingerprint image based on a difference between the foreground variation and a background estimate for the first set of regions.

The method of determining foreground variation may include: receiving first sampled foreground data in the first set of sampled data, wherein the first sampled foreground data is collected with the ultrasonic transmitter in an enabled state; receiving second sampled foreground data in the first set of sampled data, wherein the second sampled foreground data is collected with the ultrasonic transmitter in a disabled state; and calculating foreground variations for a first set of regions of the fingerprint image as a difference between the first sampled foreground data and the second sampled foreground data.

A method of performing background estimation may include: determining an updated acquisition time delay and an updated ultrasonic transmitter frequency from a change between a current temperature and a reference temperature, the initial background estimate and the initial ultrasonic transmitter frequency being determined from the reference temperature; acquiring background image information based on the updated acquisition time delay and the updated ultrasonic transmitter frequency; and calculating a background estimate using the background image information.

The method may further comprise at least one of: reducing background noise based on the autocorrelation of the pixels in the first set of regions, reducing sensor artifacts by removing static values in the first sampled data, or a combination thereof.

The method may further comprise: receiving third sample data from a third set of regions of the fingerprint image; determining a third metric level for a third set of regions indicative of enhanced prediction as to whether a finger is present using the third sampled data; and activating the apparatus based on a combination of the second metric level and the third metric level, wherein the third set of regions contains more pixels than the second set of regions, and wherein the second set of regions contains more pixels than the first set of regions.

In some implementations, a device may include: a sensor having a plurality of sensor pixels configured to sense a fingerprint image; a memory configured to store a fingerprint image; and a controller. The controller may be configured to: monitoring a first metric level for a first set of regions of a fingerprint image; determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, wherein the second set of regions contains more pixels than the first set of regions; and activating the device based on the second metric level for the second set of regions of the fingerprint image.

Drawings

The foregoing features and advantages of the invention, as well as additional features and advantages thereof, will be more clearly understood upon reading the following detailed description of embodiments of the invention, taken in conjunction with the following non-limiting and non-exhaustive aspects of the drawings. The same reference numbers are used throughout the drawings.

FIG. 1A illustrates an exemplary block diagram of a mobile device in accordance with aspects of the present invention.

FIG. 1B illustrates an exemplary implementation of a sensor subsystem of the mobile device of FIG. 1A, in accordance with aspects of the present invention.

Fig. 2 illustrates an example of a power step-by-step wake-up mechanism in accordance with aspects of the present invention.

Fig. 3 illustrates an exemplary sensor embodiment of the power step-by-step wake-up mechanism of fig. 2, in accordance with aspects of the present invention.

Fig. 4A illustrates an example of power consumption over time in the method of fig. 2, in accordance with aspects of the invention.

Fig. 4B illustrates another example of power consumption over time in the method of fig. 2, in accordance with aspects of the invention.

Fig. 4C illustrates an exemplary implementation result of the power step-wise wake-up mechanism of fig. 2, in accordance with aspects of the present invention.

Fig. 5 illustrates a method of activating a device based on detection of a fingerprint image, according to aspects of the present invention.

Fig. 6A illustrates a method of monitoring a first metric level of a first set of regions of the fingerprint image of fig. 5, in accordance with aspects of the present invention.

Fig. 6B illustrates a method of determining a second metric level for a second set of regions of the fingerprint image of fig. 5, in accordance with aspects of the present invention.

Fig. 6C illustrates a method of activating the device based on a second metric level for a second set of regions of the fingerprint image of fig. 5, in accordance with aspects of the present invention.

FIG. 6D illustrates an exemplary method of determining a metric level for a set of regions of a fingerprint image according to aspects of the present invention.

FIG. 6E illustrates an exemplary method of determining foreground variation for a set of regions of a fingerprint image in accordance with aspects of the present invention.

Fig. 6F illustrates an exemplary method of performing background estimation in accordance with aspects of the invention.

Fig. 7 illustrates an exemplary block diagram of a device that may be configured to implement a power step-wise wake-up mechanism, according to aspects of the invention.

Fig. 8A to 8C illustrate examples of ultrasonic sensors according to aspects of the present invention.

Fig. 9A illustrates an example of a four-by-four array of sensor pixels of an ultrasonic sensor array according to aspects of the invention.

Fig. 9B illustrates an example of a high-level block diagram of an ultrasonic sensor system according to aspects of the present invention.

Detailed Description

Embodiments of a power step-by-step wake-up mechanism are disclosed. The following description is presented to enable any person skilled in the art to make and use the invention. Descriptions of specific embodiments and applications are provided only as examples. Various modifications and combinations of the examples described herein will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other examples and applications without departing from the scope of the disclosure. Thus, the present invention is not intended to be limited to the examples described and illustrated, but is to be accorded the scope consistent with the principles and features disclosed herein. The word "exemplary" or "example" is used herein to mean "serving as an example, instance, or illustration. Any aspect or embodiment described herein as "exemplary" or "example" is not necessarily to be construed as preferred or advantageous over other aspects or embodiments.

FIG. 1A illustrates an exemplary block diagram of a mobile device in accordance with aspects of the present invention. In the example shown in fig. 1A, mobile device 100 may include wireless connection module 102, controller 104, sensor subsystem 106, memory 110, and application module 108. Mobile device 100 may optionally include multimedia subsystem 112, speaker and microphone 114, and display 116. In some implementations, the wireless connection module 102 may be configured to support WiFi and/or bluetooth in a wireless Local Area Network (LAN) or a wireless Personal Area Network (PAN). The controller 104 may include one or more processors, software, hardware, and firmware to implement the various functions described herein. For example, the controller 104 may be configured to implement the functions of the mobile device 100 as described in fig. 2-6. The sensor subsystem 106 may be configured to sense and process various sensor input data and generate sensor output data for the controller 104. Application module 108 may include battery charging circuitry and power manager, oscillator, phase-locked loop, clock generator, and timer.

In some implementations, the sensor subsystem 106 can be configured to sense and detect a user's finger under low power conditions. For example, the sensor subsystem 106 may be configured to include a sensor having a plurality of sensor pixels that may be configured as a low power detector (not shown), such as a 270 pixel detector configuration, to determine the energy level of certain areas of the fingerprint image and make an initial prediction as to whether a finger is present. In some implementations, the plurality of sensor pixels may be configured as an intermediate level detector, such as a 1782 pixel detector configuration, to determine energy levels for certain areas of the fingerprint image that may include sensor pixels of a low power detector configuration. The mid-level detector may be configured to make finer predictions as to whether a finger is present. In some implementations, a plurality of sensor pixels may be configured as an enhanced detector, where all pixels in the sensor are used to determine whether a finger is present using the methods described herein. The controller 104 may interface to cooperate with the low power detector configuration, the mid-level detector configuration, and/or the enhanced detector configuration to determine whether a finger is present. Controller 104 and related components of sensor subsystem 106, when engaged to cooperate with a full sensor detector, typically consume more power and require more signal processing resources than a low power detector configuration or an intermediate level detector configuration operated by sensor subsystem 106.

In certain embodiments, the mobile device 100 may include a wireless transceiver capable of transmitting and receiving wireless signals over a wireless communication network through a wireless antenna. Some embodiments may include a plurality of wireless transceivers and wireless antennas to enable transmission and/or reception of signals according to a corresponding plurality of wireless communication standards, such as versions of IEEE standard 802.11, CDMA, WCDMA, LTE, UMTS, GSM, AMPS, zigbee, bluetooth, and so forth.

The wireless connection module 102 may include an SPS receiver capable of receiving and acquiring SPS signals through an SPS antenna. The SPS receiver may also process, in whole or in part, the acquired SPS signals to estimate a position of the mobile device 100. In some embodiments, controller 104 and memory 110 may also be used in conjunction with an SPS receiver to fully or partially process acquired SPS signals, and/or to calculate an estimated position of mobile device 100. SPS or other signals used to perform positioning operations may be stored in memory 110 or registers (not shown).

In various embodiments, the controller 104 may be configured to execute one or more machine-readable instructions stored in the memory 110, such as on a computer-readable storage medium, such as RAM, ROM, flash memory, or a disk drive, to name a few examples. The one or more instructions may be executed by one or more processors, special purpose processors, or DSPs. The memory 110 may include non-transitory processor-readable memory and/or computer-readable memory that stores software code (programming code, instructions, etc.) that may be executed by the processor and/or DSP to perform the functions described herein. The controller 104 may execute instructions to perform one or more aspects of the processes/methods discussed below in connection with fig. 2-6.

In some implementations, the user interface may include any of a number of devices, such as multimedia subsystem 112, speaker and microphone 114, display 116, and so forth. In a particular implementation, the user interface may enable a user to interact with one or more applications resident on the mobile device 100. For example, the device may store analog or digital signals in memory 110 for further processing by controller 104 in response to user actions. Similarly, applications resident on the mobile device 100 may store analog or digital signals on the memory 110 to present output signals to a user.

The mobile device 100 may also include a camera for capturing still or moving images. A camera may include, for example, an imaging sensor (e.g., a charge-coupled or CMOS imager), a lens, analog-to-digital circuitry, a frame buffer, and so forth. In some implementations, additional processing, conditioning, encoding, or compression of the signals representing the captured images may be performed by the controller 104. Alternatively, the video processor may perform conditioning, encoding, compression, or manipulation of the signal representing the captured image. Additionally, the video processor may decode/decompress the stored image data for presentation on the display 116 of the mobile device 100.

FIG. 1B illustrates an exemplary implementation of a sensor subsystem of the mobile device of FIG. 1A, in accordance with aspects of the present invention. The sensor subsystem 106 may generate analog or digital signals that may be stored in the memory 110 and processed by the controller 104 to support one or more applications, such as applications related to activating a device based on detection of a fingerprint image.

As shown in fig. 1B, sensor subsystem 106 may include one or more sensor input devices 122, a sensor processing module 124, and one or more sensor output devices 126. The one or more sensor input devices 122 may include a low power (fingerprint image) detector configuration and an intermediate level (fingerprint image) detector configuration, as described above in connection with fig. 1A. The one or more sensor input devices 122 may also include one or more of the following: keys and buttons, ultrasonic sensors, temperature and humidity sensors, microphones, ultrasonic microphone arrays, photo detectors, image sensors, touch sensors, pressure sensors, chemical sensors, gyroscopes, accelerometers, magnetometers, GPS, and compasses. The sensor processing module 124 may be configured to perform one or more of the following functions, including but not limited to: input sensor selection and control, synchronization and timing control, signal processing, sensor platform performance estimation, sensor optimization, sensor fusion, and output sensor/device selection and control. The one or more sensor output devices 126 may generate one or more ultrasonic, speech, visual, biometric, proximity, presence, pressure, stability, vibration, position, orientation, heading, power, and chemical signals. Sensor subsystem 106 may be configured to implement the functions of activating a device based on the detection of a fingerprint image, as described in fig. 2-6.

The sensor processing module 124 may be configured to process sensor input data from the one or more sensor input devices 122 and generate output commands or signals for the one or more sensor output devices 126 and/or one or more optional active sensor output devices. According to aspects of the invention, direct user input may be used to predictably manipulate power control behavior. In some embodiments, the mobile device may be configured to accept user commands (through direct voice/audible and/or visual input) and configured to sense a wide range of usage uses, usage environments, and usage scenarios.

In some implementations, the sensor processing module 124 may include: an Application Specific Integrated Circuit (ASIC) including circuitry, such as a plurality of voltage regulators for generating a plurality of supply voltages; a memory, a finite state machine, a level shifter and other associated circuitry for generating control signals for an ultrasonic fingerprint sensor having a plurality of sensor pixels; circuitry for generating a transmitter excitation signal, a range gate delay signal, a diode bias signal, and a receiver bias signal for the ultrasonic sensor; circuitry for analog signal conditioning, analog-to-digital conversion, and digital processing of pixel output signals received from the ultrasonic sensor; and an interface circuit for transmitting the digital output signal to an application processor of the mobile device. The application processor may perform the methods described herein. The method may be performed on an isolated low-power island of the application processor for the purpose of minimizing power consumption, such that the entire application processor does not have to be supplied with power while in sleep mode. In the low power sleep mode, the application processor may command the ASIC to access and acquire output signals from a limited number of sensor pixels, and then the application processor may process the digitized information from the ASIC to determine if a finger is present.

In other implementations, in addition to the ASIC circuitry described in the preceding paragraph, the ASIC may include a microcontroller to autonomously execute one or more initial stages of a wake-up algorithm located locally on the ASIC. If the initial prediction of the presence of a finger is positive, a microcontroller in the ASIC may communicate with the application processor through an interrupt mechanism and wake up one or more portions of the application processor to make an intermediate or enhanced determination as to whether a finger is present. For full low power operation, the microcontroller may be required to make the determination before requesting and obtaining (enlisting) processing resources of the application processor and other components of the mobile device. In some implementations, intermediate and/or enhanced determinations as to whether a finger is present may be performed by the application processor in part by accessing and acquiring output signals from a larger set of sensor pixels, which may include the entire active area of the sensor. If the presence of a finger has been detected, fingerprint image information may be acquired and used to match the enrolled fingerprint information and authentication of the candidate user, as well as to apply other functions of the processor.

In still other implementations, the ASIC may include an array of ultrasonic sensor pixels and related circuitry for scanning the pixels, such as row drivers and column gate drivers, in addition to the microcontroller and ASIC circuitry mentioned above. In these implementations, the ASIC may perform the function of sensing the sensor pixel output signals in addition to the function of finger presence detection and other functions described herein.

Fig. 2 illustrates a power step-by-step wake-up mechanism in accordance with aspects of the present invention. In the exemplary power step-by-step wake-up mechanism shown in fig. 2, in block 202, the device is configured to monitor a first set of regions at a first sampling rate, for example using the low power detector configuration described in fig. 1A. In some embodiments, the first sampling rate may be 5Hz, 10Hz, 20Hz, 100Hz, or other sampling rate, depending on the size, resolution, power consumption, and/or other factors of the fingerprint image being monitored. In block 204, the device may be configured to estimate a first metric level for a first set of regions of the fingerprint image. In some embodiments, the first metric level is a measurement of reflected acoustic energy received at the piezoelectric receiver from the first set of regions. The first metric level may be used to indicate an initial prediction of whether an object or a user's finger has been detected. In other embodiments, other metrics and their associated metric levels may be used to detect objects or user fingers, such as fingerprint features (e.g., ridges and valleys), the presence or absence of certain spatial frequencies, acoustic impedance, and the like.

In block 206, the device may be configured to determine, e.g., by the controller 104 and/or the sensor processing module 124, whether the first metric level exceeds a first threshold. If the first metric level exceeds the first threshold (206_ yes), the method may move to block 208. Alternatively, if the first metric level does not exceed the first threshold (206_ no), the method may move back to block 202, where the process of monitoring the first metric level for the first set of regions of the fingerprint image is repeated.

In block 208, the device may be configured to monitor a second set of regions of the fingerprint image at a second sampling rate, for example, using a mid-stage detector configuration as described in fig. 1A. In some implementations, the second sampling rate may be only once, or at a frequency that depends on the size, resolution, power consumption, and/or other factors of the fingerprint image being monitored. In some implementations, the second sampling rate may be equal to or faster than the first sampling rate. In block 210, the device may be configured to estimate a second metric level for a second set of regions of the fingerprint image. In some embodiments, the second metric is a measurement of reflected acoustic energy received at the piezoelectric receiver from the second set of regions. The second metric level may be used to indicate a more refined prediction of whether an object or user's finger has been detected. In other embodiments, other metrics and their associated metric levels may be used to detect objects or user fingers, such as fingerprint features (e.g., ridges and valleys), the presence or absence of certain spatial frequencies, acoustic impedance, and the like.

In block 212, the device may be configured to determine, e.g., by the controller 104 and/or the sensor processing module 124, whether the second metric level exceeds a second threshold. If the second metric exceeds the second threshold (212_ yes), the method moves to block 214. Alternatively, if the second metric level does not exceed the second threshold (212_ no), the method may move back to block 202, where the process of monitoring the first metric level for the first set of regions of the fingerprint image is repeated.

In some embodiments, blocks 208, 210, and 212 may be bypassed in response to the first metric level exceeding the first threshold, as indicated by the dashed line from block 206 to block 214.

In block 214, the controller 104 and/or the sensor processing module 124 may determine whether a user's finger has been detected in response to the second metric level exceeding a second threshold and send a signal to activate the device in response to detecting the user's finger. Alternatively or additionally, the sensor processing module 124 may further analyze the fingerprint image of the entire active sensor area to determine if a user finger has been detected and activate the device in response to detecting the user finger.

According to aspects of the present invention, sample data may be collected from a third set of regions of the fingerprint image. In some example implementations, the controller 104 and/or the sensor processing module 124 may be configured to receive third sampled data from a third set of regions of the fingerprint image, determine a third metric level for the third set of regions indicating an enhanced prediction as to whether a finger is present using the third sampled data, and activate the device based on a combination of the second metric level and the third metric level, wherein the second set of regions includes more pixels than the first set of regions and the third set of regions includes more pixels than the second set of regions. In one approach, the third set of regions may include the entire sensing region (e.g., the entire active region) of the fingerprint image, such as the active region of an ultrasonic sensor array. For example, the first set of regions may be a 270 pixel detector configuration, the second set may be a 1782 pixel detector configuration, and the third set may be the entire active area of a 14,400 pixel detector. In this implementation, the mobile device can exit the sleep mode and be enabled (e.g., wake up) when each of the 270 pixel detector configuration, the 1782 pixel detector configuration, and the 14,400 pixel detector (entire active area) configuration exceed a threshold.

Fig. 3 illustrates an exemplary embodiment of the power step-by-step wake-up mechanism of fig. 2 in accordance with aspects of the present invention. As shown in FIG. 3, block 302 represents an exemplary fingerprint image monitored in block 202 of FIG. 2. Lines 304, 306, 308, 310, etc. represent a first set of regions of the fingerprint image sampled at a first sampling rate. As mentioned above, in this stage, a low power detector configuration, such as a 270 pixel detector configuration, the controller 104, and/or the sensor processing module 124 may be used to estimate a first metric level and compare the first metric level to a first threshold, as described in blocks 202-206 of fig. 2.

Similarly, block 312 represents the fingerprint image monitored in block 208 of FIG. 2. Clusters 314, 316, and 318 represent a second set of regions of the fingerprint image sampled at a second sampling rate. At this stage, an intermediate stage detector configuration, such as a 1782 pixel detector configuration, the controller 104, and/or the sensor processing module 124 may be used to estimate a second metric level and compare the second metric level to a second threshold, as described in blocks 208-212 of fig. 2.

In the event that the second metric level exceeds the second threshold, the presence of the user's finger may be detected and a signal may be sent by controller 104 and/or sensor processing module 124 of sensor subsystem 106 to turn on device 100. After the device 100 has been turned on, block 322 represents the monitored fingerprint image. In some embodiments, in response to the first metric level exceeding the first threshold, as described in blocks 202 through 206 of fig. 2, block 312 may be bypassed, which is indicated by the dashed line from block 302 through block 322. Block 324 represents a full sensor detector configuration, such as a 14,400 pixel detector configuration, which may be used to monitor subsequent operations, such as subsequent use of device 100. In some embodiments, the 14,400 pixel detector configuration represents the entire active area of the fingerprint sensor. In some embodiments, the fingerprint sensor may be used as a home button or other type of button in device 100.

Fig. 4A illustrates an example of power consumption over time for performing portions of the method of fig. 2, in accordance with aspects of the present invention. In this example, in the standby mode, the power consumed by sensor subsystem 106 is represented by reference numeral 402. Power is consumed to acquire samples in the first set of regions at a first sampling rate to estimate an energy level and compare the estimated energy level to a threshold. In this mode, only a small set of pixels of the fingerprint image are sampled and the amount of computation can be significantly reduced. Both of these factors contribute to reducing power consumption in the standby mode.

At time 406, assuming that a finger has been initially detected, the device may continue to perform a power step-by-step wake-up mechanism, as described in FIG. 2. Reference numeral 404 represents the power consumed by blocks 202 through 206 of fig. 2. Assuming that the first metric level exceeds the first threshold (206_ yes in fig. 2), reference numeral 408 represents the power consumed by blocks 208 through 212. If the second metric level does not exceed the second threshold (i.e., an energy deficit is detected) at time 412, the device may resume the standby mode, which is indicated by the power consumption interval represented by reference numeral 402 after time 412.

Fig. 4B illustrates another example of power consumption over time for performing portions of the method of fig. 2, in accordance with aspects of the invention. In this example, the case of the standby mode before time 406 is similar to the case of FIG. 4A.

At time 406, assuming the sensor may have sensed a finger, the device may continue to perform a power step-by-step wake-up mechanism, as described in fig. 2. Reference numeral 404 represents the power consumed by blocks 202 through 206 of fig. 2. Assuming that the first metric level exceeds the first threshold (206_ yes in fig. 2), reference numeral 410 represents the power consumed by blocks 208 through 212. In this case, if the second metric level exceeds the second threshold (i.e., sufficient energy is detected) at time 412, the presence of the user's finger may be detected and the device may be turned on. Reference numeral 414 represents the power consumption of the device after being turned on. After the device is enabled, a full sensor detector configuration, such as the full sensor detector configuration (324) shown in fig. 3, may be configured to support subsequent operations, and the controller 104 may be configured to control the full sensor detector configuration.

Fig. 4C illustrates an exemplary implementation result of the power step-wise wake-up mechanism of fig. 2, in accordance with aspects of the present invention. In this exemplary embodiment, one thousand data points of a finger touching the platen of the sensor are plotted, showing ten different finger touches, each with one hundred data points. Each data point represents a metric level calculated with either a 270 pixel detector configuration or a 1782 pixel detector configuration with the finger on the sensor (plot points 420 and 424) or with the finger off the sensor (plot points 422 and 426, respectively). The threshold used (e.g., the first threshold and the second threshold) is 0.9972. As shown in FIG. 4C, plot 420 represents the result of a low power 270 pixel detector configuration with a finger on the sensor; plot 424 represents the result of an intermediate level 1782 pixel detector configuration with a finger on the sensor; plot point 422 represents the result of a low-power 270 pixel detector configuration without a finger on the sensor; and plot 426 represents the result of the mid-level 1782 pixel detector configuration with a finger not on the sensor. When the threshold is 0.9972, the metric levels of the 270 pixel detector configuration and the 1782 pixel detector configuration can be clearly distinguished between finger on sensor and finger off sensor, with the 1782 pixel detector configuration showing less variation and higher separation.

Fig. 5 illustrates a method of activating a device based on detection of a fingerprint image, according to aspects of the present invention. As shown in fig. 5, in block 502, the method may monitor a first metric level for a first set of regions of a fingerprint image. In block 504, the method may determine a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold. In block 506, the method may activate the device based on a second metric level for a second set of regions of the fingerprint image. In some implementations, the second set of regions can correspond to a portion of sensor pixels in the sensor. In some implementations, the second set of regions can correspond to the entire active area of the sensor (e.g., all sensor pixels). Optionally, the method may monitor a third region of the fingerprint image and implement a user interface operation with the device using the third region of the fingerprint image. The third region may correspond to a third set of regions, which in some implementations may be the entire active region of the sensor.

According to aspects of the invention, the first metric level may correspond to at least one of an acoustic energy level, an acoustic loading level, a spatial frequency, a cross-correlation value, an image quality value, or some combination thereof. In some implementations, the acoustic energy level may be determined by comparing output signals from one or more sensor pixels in the first set of regions to a background or static value acquired with the ultrasonic transmitter turned off (e.g., disabled) and calculating a first metric level from a difference in the output signals. In some implementations, the acoustic energy level may be determined by comparing output signals from one or more sensor pixels in the first set of regions to foreground values acquired with the ultrasonic transmitter turned on (e.g., enabled) and calculating a first metric level from a difference in the output signals. The presence or absence of a finger on the surface of a platen coupled to the ultrasonic sensor affects the acoustic energy level of the received signal. In some implementations, the acoustic load level may be determined by comparing statistics (e.g., an average, a weighted average, a standard deviation, etc.) of the output signals from one or more sensor pixels in the first set of regions with background statistics determined if the ultrasound is off or foreground statistics determined if the ultrasound transmitter is on. The presence or absence of a finger affects the acoustic load level. In some embodiments, the spatial frequencies may be determined from output signals acquired from a plurality of pixels in the first set of regions by performing a Fast Fourier Transform (FFT) on the acquired output signals. For example, a spatial frequency in the range of one to five line pairs per millimeter, or more closely in the range of two to three line pairs per millimeter, may indicate the presence or absence of fingerprint ridges and valleys, and thus the presence or absence of a finger. In some implementations, the cross-correlation value may be determined by comparing output signals from a set of one or more pixels in the first set of regions with a neighboring set of one or more pixels in the first set of regions. The absence of a finger tends to cause noise and/or random variations to be detected between adjacent pixels or adjacent groups of one or more pixels, but the presence of a finger may cause significant signal differences to occur between adjacent pixels or adjacent groups of one or more pixels due to finger ridges and valleys or other textures of objects positioned relative to the platen. In some implementations, the image quality value may be determined from output signals acquired from one or more pixels in the first set of regions. For example, the image quality value may correspond to a contrast ratio between an area that may represent a finger ridge line and an area that may represent a finger valley line. In another example, the image quality value may correspond to a rate of change of pixel output signals from one pixel to the next or from one group of pixels to the next, indicating good feature definition.

In some implementations, more than one metric level may be combined to form a composite metric level, which may provide a better determination as to whether a finger is present. In some embodiments, the second metric level may be determined in a manner similar to the determination of the first metric level. In some embodiments, the second metric level may have a similar threshold as the first metric level; but in other implementations the second metric level may have a higher threshold.

According to aspects of the invention, the first set of regions may correspond to sensor pixels selected from one of: a set of lines (e.g., a set of rows), a set of partial lines, a set of columns, a set of partial columns, a set of blocks, a set of split pixels, a continuous line, a continuous partial line, a continuous column, a continuous partial column, a continuous block, a continuous sub-block, a set of continuous regions, a set of discontinuous regions, or some combination thereof. The first set of regions may be centered on the active area of the ultrasonic sensor array. In some implementations, the center of the first set of regions can be located over the active region to preferentially detect fingers positioned over the active region and to reduce detection of fingers positioned only over the edges of the active region.

In some embodiments, the second set of regions may correspond to sensor pixels selected from one of: a set of lines (e.g., a set of rows), a set of partial lines, a set of columns, a set of partial columns, a set of blocks, a set of separate pixels, a continuous line, a continuous partial line, a continuous column, a continuous partial column, a continuous frame, a continuous sub-block, a set of continuous regions, a set of discontinuous regions, an entire active region, or some combination thereof. The second set of regions may be centered on the active area of the ultrasonic sensor array. The second set of regions typically contains more pixels than the first set of regions. The block or sub-block of sensor pixels may include a rectangular pixel array with two or more adjacent pixels in a first direction within the pixel array and two or more adjacent pixels in a second direction perpendicular to the first direction.

Fig. 6A illustrates a method of monitoring a first metric level for a first set of regions of a fingerprint image, as shown in block 502 of fig. 5, in accordance with aspects of the present invention. In the example shown in fig. 6A, in block 602, the method may receive first sample data for a first set of regions of a fingerprint image at a first sample rate. In some implementations, the first sampling rate may be five frames or partial frames per second for a sampling rate of 5 Hz.

In block 604, the method may determine a first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data. In optional block 606, the method may monitor a first metric level for a first set of regions of the fingerprint image in response to the first metric level being less than or equal to a first threshold. In some implementations, the first set of regions of the fingerprint image may include a set of pixels arranged along a plurality of lines, where the set of pixels may include a 270 pixel pattern. The 270 pixel pattern may include five lines with 54 pixels per line, and wherein each line may include three line segments with 18 pixels per line segment.

Fig. 6B illustrates a method of determining a second metric level for a second set of regions of the fingerprint image as shown in block 504 of fig. 5, in accordance with aspects of the present invention. As shown in fig. 6B, in block 612, the method may receive second sample data for a second set of regions of the fingerprint image at a second sample rate. The second sampling rate may be one time. In some implementations, the second sampling rate may be only once, or at a frequency that depends on the size, resolution, power consumption, and/or other factors of the fingerprint image being monitored. In some implementations, the second sampling rate may be equal to or faster than the first sampling rate. In block 614, the method may determine a second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data. In some implementations, the second set of regions of the fingerprint image may include a set of pixels arranged in a plurality of clusters, where the set of pixels may include a 1782 pixel pattern. In some implementations, the 1782 pixel pattern can include three sub-blocks of pixels, where each sub-block is 18 pixels by 33 pixels in size.

Fig. 6C illustrates a method of activating a device based on a second metric level for a second set of regions of the fingerprint image, as shown in block 506 of fig. 5, in accordance with aspects of the present invention. In the embodiment of fig. 6C, in block 622, the method may determine the presence of the finger in response to the second metric exceeding a second threshold. In block 624, the method may activate the device in response to determining the presence of a finger. In optional block 626, the method may monitor a first metric level for a first set of regions of the fingerprint image in response to the second metric level being less than or equal to a second threshold.

FIG. 6D illustrates an exemplary method of determining a metric level for a set of regions of a fingerprint image according to aspects of the present invention. In the embodiment of fig. 6D, in block 632, the method may determine foreground variation based on the presence of the fingerprint image. In block 634, the method may perform a background estimation of a first set of regions of the fingerprint image. In block 636, the method may determine a first metric level for a first set of regions of the fingerprint image based on a difference between the foreground variation and a background estimate for the first set of regions.

FIG. 6E illustrates an exemplary method of determining foreground variation for a set of regions of a fingerprint image in accordance with aspects of the present invention. In the embodiment of fig. 6E, in block 642, the method may receive first sampled foreground data in the first set of sampled data, wherein the first sampled foreground data is collected with the ultrasonic transmitter in an enabled state (also referred to as an on state). In block 644, the method may receive second sampled foreground data in the first set of sampled data, wherein the second sampled foreground data is collected with the ultrasound transmitter in a disabled state (also referred to as an off state). In block 646, the method may calculate foreground variations for the regional set of fingerprint images as a difference between the first sampled foreground data and the second sampled foreground data. It should be noted that the difference between the first sampled foreground data and the second sampled foreground data may be configured to reduce the signal due to thermoelectric effects introduced when a finger/object touches or is positioned near the piezoelectric layer of the ultrasonic sensor. The thermoelectric effect may result from the ability of certain materials (e.g., piezoelectric materials) to generate a temporary voltage as they heat or cool. The change in temperature slightly changes the position of the atoms within the crystal structure, thereby changing the polarization of the material. This polarization change creates a surface charge on the surface of the pyroelectric material and a voltage across the crystal. If the temperature remains unchanged at its new value, the thermoelectric voltage will gradually disappear due to charge leakage. The leakage may be due to electrons moving through the crystal, ions moving through air, current leaking through a voltmeter attached across the crystal, and so forth. By reducing or eliminating the thermoelectric effect, a more accurate ultrasonic signal can be obtained.

Fig. 6F illustrates an exemplary method of performing background estimation in accordance with aspects of the invention. In the embodiment of fig. 6F, in block 652, the method may determine an updated acquisition time delay and an updated ultrasonic transmitter frequency from the change in the current temperature relative to the reference temperature from which the initial background estimate and the initial ultrasonic transmitter frequency may be determined. In block 654, the method may acquire background image information based on the updated acquisition time delay and the updated ultrasound transmitter frequency. In block 656, the method may calculate a background estimate using the background image information.

Optionally or additionally, the method may perform at least one of: reducing background noise based on auto-correlation of pixels in the region group (block 658); reducing sensor artifacts by removing static values in the sampled data (block 660); or a combination thereof. In one implementation, the autocorrelation of the pixels in the regional group may be performed with one pixel shifted or lagged in the horizontal direction in the fingerprint image shown in FIG. 3.

It should be noted that the methods described in fig. 6D-6F may be used to determine a first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data and a second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data.

Fig. 7 illustrates an exemplary block diagram of a device that may be configured to implement a power step-wise wake-up mechanism, according to aspects of the invention. A device that may implement a power gradual wake-up mechanism may include one or more features of the mobile device 700 shown in fig. 7. In certain embodiments, the mobile device 700 may include a wireless transceiver 721, the wireless transceiver 721 being capable of transmitting and receiving wireless signals 723 over a wireless communication network through a wireless antenna 722. Wireless transceiver 721 may be connected to bus 701 by a wireless transceiver bus interface 720. In some embodiments, wireless transceiver bus interface 720 may be at least partially integrated with wireless transceiver 721. Some embodiments may include a plurality of wireless transceivers 721 and wireless antennas 722 to enable transmission and/or reception of signals according to a corresponding plurality of wireless communication standards, such as versions of the IEEE standard 802.11, CDMA, WCDMA, LTE, UMTS, GSM, AMPS, zigbee, and zigbee standardsAnd the like.

The mobile device 700 may also include a GPS receiver 755, which GPS receiver 755 is capable of receiving and acquiring GPS signals 759 through a GPS antenna 758. The GPS receiver 755 may also process, in whole or in part, the acquired GPS signals 759 to estimate the location of the mobile device. In some embodiments, the processor 711, memory 740, DSP 712, and/or a dedicated processor (not shown) may also be used in conjunction with the GPS receiver 755 to process the acquired GPS signals, in whole or in part, and/or calculate an estimated location of the mobile device 700. Storage of GPS or other signals may be performed in memory 740 or registers (not shown).

Also shown in fig. 7, mobile device 700 may include: a Digital Signal Processor (DSP)712 coupled to bus 701 through bus interface 710, a processor 711 coupled to bus 701 through bus interface 710, and a memory 740. The bus interface 710 may be integrated with the DSP 712, the processor 711, and the memory 740. In various embodiments, functions may be performed in response to execution of one or more machine-readable instructions stored in memory 740, such as on a computer-readable storage medium, e.g., RAM, ROM, flash memory, or a disk drive, to name a few. One or more instructions may be executed by the processor 711, special purpose processor, or DSP 712. The memory 740 may include non-transitory processor-readable memory and/or computer-readable memory that stores software code (programming code, instructions, etc.) that may be executed by the processor 711 and/or the DSP 712 to perform the functions described herein. In a particular implementation, the wireless transceiver 721 may communicate with the processor 711 and/or the DSP 712 over the bus 701 to enable the mobile device 700 to be configured as a wireless station. The processor 711 and/or DSP 712 may perform the methods and functions and execute instructions to perform one or more aspects of the processes/methods discussed in connection with fig. 1-6F and 8-9B.

Also shown in fig. 7, the user interface 735 may include any of a number of devices, such as a speaker, a microphone, a display device, a vibration device, a keyboard, a touch screen, and so forth. The user interface signals provided to the user may be one or more outputs provided through any of a speaker, a microphone, a display device, a vibration device, a keyboard, a touch screen, and the like. In a particular implementation, the user interface 735 may enable a user to interact with one or more applications resident on the mobile device 700. For example, the devices of the user interface 735 may store analog or digital signals on the memory 740 for further processing by the DSP 712 or the processor 711 in response to user actions. Similarly, applications resident on mobile device 700 may store analog or digital signals on memory 740 to present output signals to a user. In another implementation, the mobile device 700 may optionally include a dedicated audio input/output (I/O) device 770 including, for example, a dedicated speaker, a microphone, digital-to-analog circuitry, analog-to-digital circuitry, an amplifier, and/or a gain control. In another implementation, the mobile device 700 may include a touch sensor 762 that responds to touch, pressure, or ultrasonic signals on a keyboard or touchscreen device.

The mobile device 700 may also include a dedicated camera device 764 for capturing still or moving images. The dedicated camera device 764 may include, for example, an imaging sensor (e.g., a charge-coupled device or CMOS imager), a lens, analog-to-digital circuitry, a frame buffer, and so forth. In one implementation, additional processing, conditioning, encoding, or compression of the signals representing the captured images may be performed at the processor 711 or DSP 712. Alternatively, the dedicated video processor 768 may perform conditioning, encoding, compression, or manipulation of the signal representing the captured image. Additionally, the dedicated video processor 768 may decode/decompress stored image data for presentation on a display device (not shown) on the mobile device 700.

The mobile device 700 may also include sensors 760 coupled to the bus 701, the sensors 760 including, for example, inertial sensors and environmental sensors. The inertial sensors of sensors 760 may include, for example, accelerometers (e.g., collectively responsive to acceleration of mobile device 700 in three dimensions), one or more gyroscopes or one or more magnetometers (e.g., supporting one or more compass applications). Environmental sensors of the mobile device 700 can include, for example, temperature sensors, barometric pressure sensors, ambient light sensors, as well as camera imagers, microphones, to name a few. The sensor 760 may generate analog or digital signals that may be stored in the memory 740 and processed by the DPS or the processor 711 to support one or more applications, such as applications directed to positioning or navigation operations.

In a particular implementation, the mobile device 700 may include a dedicated modem processor 766 capable of performing baseband processing on signals received and downconverted at the wireless transceiver 721 or the GPS receiver 755. Similarly, dedicated modem processor 766 may perform baseband processing on signals to be upconverted for transmission by wireless transceiver 721. In an alternative implementation, instead of having a dedicated modem processor, baseband processing may be performed by a processor or DSP (e.g., processor 711 or DSP 712).

Fig. 8A to 8C illustrate examples of ultrasonic sensors according to aspects of the present invention. As shown in fig. 8A, the ultrasonic sensor 10 may include an ultrasonic transmitter 20 and an ultrasonic receiver 30 under a platen 40. The ultrasonic transmitter 20 may be a piezoelectric transmitter, which may generate ultrasonic waves 21 (see fig. 8B). The ultrasonic receiver 30 may include a piezoelectric material and an array of pixel circuits disposed in or on a substrate. In some implementations, the substrate can be a glass, plastic, or semiconductor substrate, such as a silicon substrate. In operation, the ultrasonic transmitter 20 may generate one or more ultrasonic waves that are advanced through the ultrasonic receiver 30 to the exposed surface 42 of the platen 40. At the exposed surface 42 of the platen 40, ultrasonic energy may be emitted, absorbed, or scattered by, or reflected back from, an object 25 (e.g., skin having fingerprint ridges 28) contacting the platen 40. In those locations where air contacts the exposed surface 42 of the platen 40 (e.g., valleys 27 between fingerprint ridges 28), most of the ultrasonic waves will be reflected back toward the ultrasonic receiver 30 for detection (see fig. 8C). The control electronics 50 may be coupled to the ultrasonic transmitter 20 and the ultrasonic receiver 30, and may supply timing signals that cause the ultrasonic transmitter 20 to generate one or more ultrasonic waves 21. The control electronics 50 may then receive a signal from the ultrasonic receiver 30 indicative of the reflected ultrasonic energy 23. The control electronics 50 may use the output signals received from the ultrasonic receiver 30 to construct a digital image of the object 25. In some embodiments, the control electronics 50 may also continuously sample the output signal over time to detect the presence and/or movement of the object 25.

According to aspects of the present disclosure, the ultrasonic transmitter 20 may be a plane wave generator including a substantially planar piezoelectric transmitter layer. The plane wave may be generated by applying a voltage to the piezoelectric layer to expand or contract the layer in accordance with the applied signal to generate an ultrasonic wave. A voltage may be applied to the piezoelectric transmitter layer by the first transmitter electrode and the second transmitter electrode. In this way, ultrasound can be generated by straining the thickness of the layer via the piezoelectric. This ultrasonic wave is advanced toward the finger (or other object to be detected) and transmitted through the platen 40. A portion of the waves that are not absorbed or transmitted by the object to be detected may be reflected for transmission back through the platen 40 and received by the ultrasonic receiver 30. The first and second transmitter electrodes may be metallized electrodes, e.g. metal layers coating opposite sides of the piezoelectric transmitter layer.

The ultrasonic receiver 30 may include: an array of pixel circuits disposed in or on a substrate, which may also be referred to as a wafer or backplane; and a piezoelectric receiver layer. In some implementations, each pixel circuit may include one or more silicon or Thin Film Transistor (TFT) elements, electrical interconnect traces, and in some implementations, one or more additional circuit elements, such as diodes, capacitors, and the like. Each pixel circuit may be configured to convert charge generated in a piezoelectric receiver layer proximate the pixel circuit into an electrical signal. Each pixel circuit may include a pixel input electrode that electrically couples the piezoelectric receiver layer to the pixel circuit.

In the illustrated implementation, the receiver bias electrode is disposed on a side of the piezoelectric receiver layer proximate to the platen 40. The receiver bias electrode may be a metallized electrode and may be grounded or biased to control which signals are passed to the silicon or TFT sensor array. Ultrasonic energy reflected from the exposed (top) surface 42 of the platen 40 is converted into localized electrical charges by the piezoelectric receiver layer. These localized charges are collected by the pixel input electrodes and transferred onto the underlying pixel circuitry. The charge may be amplified by the pixel circuit and provided to control electronics, which process the output signal. A simplified schematic diagram of an example pixel circuit is shown in fig. 9A, but one of ordinary skill in the art will appreciate that many variations and modifications to the example pixel circuit shown in the simplified schematic diagram are contemplated.

The control electronics 50 may be electrically connected to the first and second transmitter electrodes, as well as the receiver bias electrode and the pixel circuitry in or on the substrate. The control electronics 50 may operate substantially as previously discussed with respect to fig. 8A-8C.

The platen 40 may be any suitable material that can be acoustically coupled to a receiver, examples include plastic, ceramic, glass, sapphire, stainless steel, aluminum, metal alloys, polycarbonate, polymeric materials, or metal-filled plastics. In some implementations, the platen 40 may be a cover plate, such as a cover glass or lens glass for a display device or an ultrasonic sensor. Inspection and imaging can be performed by a relatively thick (e.g., 3mm and above) platen, if desired.

Examples of piezoelectric materials that may be employed in accordance with various embodiments include piezoelectric polymers having suitable acoustic properties, for example, an acoustic impedance between about 2.5 and 5 mrayls. Specific examples of piezoelectric materials that can be employed include ferroelectric polymers such as polyvinylidene fluoride (PVDF) and polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE) copolymers. Examples of PVDF copolymers include 60:40 (mole percent) PVDF-TrFE, 70:30 PVDF-TrFE, 80:20 PVDF-TrFE, and 90:10 PVDR-TrFE. Other examples of piezoelectric materials that may be employed include polyvinylidene chloride (PVDC) homopolymers and copolymers, Polytetrafluoroethylene (PTFE) homopolymers and copolymers, and diisopropylamine bromide (DIPAB).

The thickness of each of the piezoelectric transmitter layer and the piezoelectric receiver layer may be selected to be suitable for generating and receiving ultrasonic waves. In one example, the thickness of the PVDF piezoelectric transmitter layer may be about 28 μm, and the thickness of the PVDF-TrFE receiver layer may be about 12 μm. An example frequency of the ultrasonic waves is in the range of 5MHz to 30MHz, with a wavelength of about a quarter of a millimeter or less.

Fig. 8A to 8C show example arrangements of ultrasonic transmitters and receivers in an ultrasonic sensor, but other arrangements are possible. For example, in some embodiments, the ultrasonic transmitter 20 may be above the ultrasonic receiver 30, i.e., closer to the test object. In some implementations, the piezoelectric receiver layer can function as both an ultrasonic transmitter and an ultrasonic receiver. Piezoelectric layers that can be used as ultrasonic transmitters or ultrasonic receivers can be referred to as piezoelectric transceiver layers or single layer transmitter/receiver layers. In some implementations, the ultrasonic sensor may include an acoustic delay layer. For example, an acoustic delay layer may be incorporated within the ultrasonic sensor 10 between the ultrasonic transmitter 20 and the ultrasonic receiver 30. The acoustic delay layer can be used to adjust the ultrasonic pulse timing and at the same time electrically isolate the ultrasonic receiver 30 from the ultrasonic transmitter 20. The delay layer may have a substantially uniform thickness, with the material for the delay layer and/or the thickness of the delay layer being selected to provide a desired delay in the time for the reflected ultrasonic energy to reach the ultrasonic receiver 30. In doing so, it is possible to make the energy pulses carrying information about the object by virtue of having been reflected by the object reach the time range during which the energy reflected from the other parts of the ultrasonic sensor 10 is unlikely to reach the ultrasonic receiver 30. In some implementations, a silicon or TFT substrate and/or platen 40 may be used as the acoustic delay layer.

Fig. 9A depicts a 4 x 4 pixel array of pixels of an ultrasonic sensor. Each pixel may be associated with a local area of piezoelectric sensor material, a peak detection diode, and a readout transistor, for example; many or all of these elements may be formed on or in the backplane to form pixel circuitry. In practice, a localized area of piezoelectric sensor material per pixel may transform received ultrasonic energy into an electrical charge. The peak detection diode may register the maximum amount of charge detected by a localized area of piezoelectric sensor material. Each row in the pixel array may then be scanned, for example, by a row select mechanism, gate driver, or shift register, and the readout transistor of each column may be triggered to allow additional circuitry (e.g., multiplexers and a/D converters) to be able to read the peak charge magnitude of each pixel. The pixel circuit may include one or more silicon transistors or TFTs to allow gating, addressing, and resetting of the pixels.

Each pixel circuit may provide information about a small portion of an object detected by the ultrasonic sensor 10. Although the example shown in fig. 9A has a relatively coarse resolution for convenience of illustration, an ultrasonic sensor having a resolution of about 500 pixels per inch or higher may be configured with a layered structure. The detection area of the ultrasonic sensor 10 can be selected according to a given detection object. For example, the detection area (e.g., active area) may range from about 5mm x 5mm for a single finger to about 3 inches x3 inches for four fingers. Smaller and larger areas, including square, rectangular and non-rectangular geometries, may be used depending on the needs of the object.

FIG. 9B illustrates an example of a high-level block diagram of an ultrasonic sensor system. Many of the elements shown may form part of the control electronics 50. The sensor controller may include a control unit configured to control various aspects of the sensor system, such as ultrasonic transmitter timing and excitation waveforms, bias voltages for ultrasonic receivers and pixel circuits, pixel addressing, signal filtering and conversion, readout frame rates, and so forth. The sensor controller may also include a data processor that receives data from the ultrasonic sensor circuit pixel array. The data processor may convert the digitized data into image data of a fingerprint or format the data for further processing.

For example, the control unit may send a transmitter (Tx) excitation signal to the Tx driver at regular intervals in order to cause the Tx driver to excite the ultrasonic transmitter and generate a planar ultrasonic wave. The control unit may send a level selection input signal through a receiver (Rx) bias driver to bias the receiver bias electrodes and allow gating of acoustic signal detection through the pixel circuitry. A demultiplexer may be used to open and close the gate driver so that a particular row or column of sensor pixel circuits provides a sensor output signal. The output signals from the pixels may be sent to a data processor through a charge amplifier, a filter such as an RC filter or an anti-aliasing filter, and a digitizer. It should be noted that portions of the system may be included on a silicon or TFT substrate and other portions may be included in an associated integrated circuit (e.g., ASIC).

According to aspects of the present disclosure, an ultrasonic sensor may be configured to generate high resolution fingerprint images for user verification and authentication. In some implementations, the ultrasonic fingerprint sensor may be configured to detect a reflected signal that is proportional to the differential acoustic impedance between the outer surface of the platen and the finger ridges (tissue) and valleys (air). For example, a portion of the ultrasonic energy of the ultrasonic waves may be emitted from the sensor into the finger tissue in the ridge region while the remainder of the ultrasonic energy is reflected back toward the sensor, while a smaller portion of the waves may be emitted into the air in the valley region of the finger while the remainder of the ultrasonic energy is reflected back to the sensor. The methods of correcting for diffraction effects disclosed herein can increase the overall signal and image contrast from the sensor.

It should be noted that at least the following three paragraphs, fig. 1-2, fig. 5-9 and their corresponding descriptions provide: means for monitoring a first metric level for a first set of regions of the fingerprint image; means for determining a second metric level for a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, wherein the second set of regions includes more pixels than the first set of regions; means for activating the means based on a second metric level for a second set of regions of the fingerprint image; means for receiving first sample data from a first set of regions of a fingerprint image at a first sample rate; means for determining a first metric level for indicating an initial prediction as to whether a finger is present using the first sampled data; means for receiving second sample data from a second set of regions of the fingerprint image; means for determining a second metric level for indicating a finer prediction as to whether a finger is present using the second sampled data; means for determining whether a finger is present in response to the second metric exceeding a second threshold; means for activating the device in response to the presence of a finger; means for determining foreground variation based on fingerprint image presence; means for performing a background estimation of a first set of regions of the fingerprint image; and means for determining a first metric level for a first set of regions of the fingerprint image based on a difference between the foreground variation and a background estimate for the first set of regions.

The methods described herein may be implemented by various means depending on the application according to a particular example. For example, the methods may be implemented in hardware, firmware, software, or any combination thereof. For example, in a hardware implementation, a processing unit may be implemented within one or more application specific integrated circuits ("ASICs"), digital signal processors ("DSPs"), digital signal processing devices ("DSPDs"), programmable logic devices ("PLDs"), field programmable gate arrays ("FPGAs"), processors, controllers, micro-controllers, microprocessors, electronic devices, other device units designed to perform the functions described herein, or a combination thereof.

Some portions of the detailed description contained herein are presented in terms of algorithms or symbolic representations of operations on binary digital signals stored within a memory of a particular apparatus or special purpose computing device or platform. In the context of this particular specification, the term "particular apparatus" or the like includes a general purpose computer (as long as it is programmed to perform particular operations pursuant to instructions from program software). Algorithmic descriptions or symbolic representations are examples of techniques used by those skilled in the signal processing or related arts to convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of operations or similar signal processing leading to a desired result. In this context, operations or processing involve physical manipulation of physical quantities. Usually, though not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, labels, or the like. It should be understood, however, that all of these or similar terms are to be associated with the appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as apparent from the discussion herein, it is appreciated that throughout the description, discussions utilizing terms such as "processing," "computing," "calculating," "determining," or the like, refer to the action and processes of a specific apparatus, such as a special purpose computer, special purpose computing apparatus, or similar special purpose electronic computing device. In the context of this specification, therefore, a special purpose computer or a similar special purpose electronic computing device is capable of manipulating or transforming signals, typically represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose electronic computing device.

The wireless communication techniques described herein may be incorporated with various wireless communication networks, such as a wireless wide area network ("WWAN"), a wireless local area network ("WLAN"), a Wireless Personal Area Network (WPAN), and so on. The terms "network" and "system" may be used interchangeably herein. The WWAN may be a code division multiple access ("CDMA") network, a time division multiple access ("TDMA") network, a frequency division multiple access ("FDMA") network, an orthogonal frequency division multiple access ("OFDMA") network, a single carrier frequency division multiple access ("SC-FDMA") network, or any combination thereof, among others. A CDMA network may implement one or more radio access technologies ("RATs"), such as CDMA2000, wideband CDMA ("W-CDMA"), to name just a few radio technologies. Here, cdma2000 may include technologies implemented in accordance with IS-95, IS-2000, and IS-856 standards. The TDMA network may implement Global System for Mobile communications ("GSM"), digital advanced Mobile Phone System ("D-AMPS"), or some other RAT. GSM and W-CDMA are described in documents from an association entitled "third generation partnership project" ("3 GPP"). Cdma2000 is described in a document from an association entitled "third generation partnership project 2" ("3 GPP 2"). The 3GPP and 3GPP2 documents are publicly available. In an aspect, a 4G long term evolution ("LTE") communication network may also be implemented in accordance with claimed subject matter. For example, a WLAN may include an IEEE802.11x network, and a WPAN may include a Bluetooth network, IEEE 802.15 x. The wireless communication implementations described herein may also be used in connection with any combination of WWAN, WLAN or WPAN.

In another aspect, as previously mentioned, the wireless transmitter or access point may include a femtocell for extending cellular telephone service into a business or home. In such an implementation, one or more mobile devices may communicate with a femtocell by, for example, a code division multiple access ("CDMA") cellular communication protocol, and the femtocell may provide the mobile device with access to a larger cellular telecommunications network by way of another broadband network, such as the internet.

The terms "and" or "as used herein may include a variety of meanings that will depend at least in part on the context in which the term is used. Generally, "or," if used in association lists, such as A, B or C, is intended to mean A, B and C (used herein in an inclusive sense), and A, B or C (used herein in an exclusive sense). Reference throughout this specification to "one example" or "an example" means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of claimed subject matter. Thus, the appearances of the phrase "in one example" or "an example" in various places throughout this specification are not necessarily all referring to the same example. Furthermore, the particular features, structures, or characteristics may be combined in one or more examples. Examples described herein may include a machine, device, engine, or apparatus that operates using digital signals. Such signals may include electronic signals, optical signals, electromagnetic signals, or any form of energy that provides information between locations.

While there has been illustrated and described what are presently considered to be example features, it will be understood by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from claimed subject matter. In addition, many modifications may be made to adapt a particular situation to the teachings of claimed subject matter without departing from the central concept described herein. Therefore, it is intended that claimed subject matter not be limited to the particular examples disclosed, but that such claimed subject matter may also include all aspects falling within the scope of the appended claims, and equivalents thereof.