阅读说明：本技术 一种生物传感器系统及其应用于检测生物样品的方法 (Biosensor system and method for detecting biological sample by using same ) 是由 王嘉威 徐小川 何枫 段嘉楠 于 2021-08-10 设计创作，主要内容包括：本发明公开了一种生物传感器系统及用于检测生物样品的方法,系统包括传感器芯片、微流控通道、成像单元；所述传感器芯片上设有光波导、微谐振腔及片上分束器、片上耦合光栅结构,所述光波导、分束器及微谐振腔结构共同形成光耦合、光传导、以及光共振装置；所述传感器芯片与所述微流控通道集成在一起。所述生物传感器系统基于光子集成芯片面外散射成像,同时以图像识别分析为技术路径进行生物样品的信号判定以及浓度检测。本发明解决现有方法存在的技术缺陷,使系统满足高速、高效、低成本的检测要求。(The invention discloses a biosensor system and a method for detecting biological samples, wherein the system comprises a sensor chip, a microfluidic channel and an imaging unit; the sensor chip is provided with an optical waveguide, a micro resonant cavity, an on-chip beam splitter and an on-chip coupling grating structure, wherein the optical waveguide, the beam splitter and the micro resonant cavity structure together form an optical coupling, optical conduction and optical resonance device; the sensor chip is integrated with the microfluidic channel. The biosensor system is based on photonic integrated chip out-of-plane scattering imaging, and meanwhile, signal judgment and concentration detection of a biological sample are carried out by taking image recognition analysis as a technical path. The invention solves the technical defects of the prior method, and ensures that the system meets the detection requirements of high speed, high efficiency and low cost.)

1. A biosensor system, comprising:

the sensor chip is provided with an optical waveguide and an on-chip micro-resonant cavity structure, and the optical waveguide and the on-chip micro-resonant cavity structure jointly form an optical coupling device, an optical conduction device and an optical resonance device;

a microfluidic channel integrated with the sensor chip;

and the imaging unit is used for acquiring scattering signals after optical coupling, optical conduction and optical resonance.

2. The biosensor system of claim 1, wherein said microfluidic channel is a Polydimethylsiloxane (PDMS) microfluidic channel.

3. The biosensor system according to claim 1, wherein the sensor chip and the microfluidic channel are integrated by a method comprising:

adopting a silicon or photoresist structure with a photoetching defined pattern as a template, mixing basic components and a curing agent in Dow Corning SYLGARD184 in a container with a mold in a microfluidic channel, uniformly stirring, then placing in a vacuum box to remove bubbles, standing for curing, and finally stripping out the microfluidic channel;

the fluid input and output interfaces of the microfluidic channel are obtained through a puncher and are connected with an external fluid pump by inserting a metal connecting pipe;

under the assistance of optical microscope imaging, the sensor chip and the microfluidic channel are subjected to oxygen plasma treatment, so that irreversible bonding is formed on the surfaces of the sensor chip and the microfluidic channel, and the space alignment between the microfluidic channel with the micron scale and the sensor chip is obtained.

4. The biosensor system according to claim 1, wherein said imaging unit comprises any one of an objective lens and a micro lens, and any one of a CMOS image sensor and a CCD image sensor.

5. A biosensor system according to claim 1, wherein said imaging unit is an integrated device with a light source and a camera.

6. The biosensor system of claim 1, wherein said optical waveguide is a silicon nitride material.

7. The biosensor system according to claim 1, wherein the sensor chip comprises a plurality of sensing units, each of which has a micro-ring structure, and the micro-ring structure has any one of a circular type, a racetrack type and a spiral type.

8. The biosensor system of claim 7, wherein said sensor chip further comprises a trunk optical waveguide and a multimode waveguide splitter for optical input of each sensing unit.

9. A method for detecting a biological sample using the biosensor system according to any one of claims 1 to 8, the method comprising:

firstly, performing surface functionalization on an externally exposed part of a sensor chip, and fixing specific receptors aiming at multiple biomarkers in channels;

loading the sensor chip into an imaging unit, introducing light source exciting light into the main trunk waveguide, and simultaneously performing out-of-plane scattering imaging in the top view direction of the sensor chip;

introducing a solution to be detected by adopting a fluid pump;

acquiring real-time scattering signals by an imaging unit to obtain an out-of-plane scattering image;

and step five, analyzing the pixel matrix of the out-of-plane scattering image as input by using the established deep learning algorithm to obtain the real-time concentration change information of the object to be detected, and finally obtaining a dynamic analysis result.

10. The method of detecting a biological sample according to claim 9, further comprising continuously introducing a solution labeled with specific antibody-linked metal nanoparticles for scattered signal amplification after introducing the solution to be detected using a fluid pump.

Technical Field

The present invention relates to the field of biological sample detection, and more particularly, to a biosensor system and a method for detecting a biological sample.

Background

The measurement of intrinsic properties (such as size, weight, morphology) of biological samples directly under fluorescence label-free conditions has now been developed as an important approach essential for basic research in life science and clinical testing.

The existing fluorescence-labeling-free optical sensing chip technology mainly represented by a plasmon structure and an on-chip integrated optical guided wave structure is developed for many years, and multiple working mechanisms for extracting resonance spectrum information change are realized, wherein the multiple working mechanisms mainly comprise monitoring of resonance peak red shift under wavelength resolution and angle resolution. However, most solutions rely on sophisticated devices, such as narrow linewidth, wavelength tunable lasers, spectrometers, piezoelectric driven angularly-displaced stages, etc., and more discrete devices in the optical path. The measurement processes such as spectrum scanning and angle scanning also limit the time resolution of measurement, and the potential noise caused by unstable light source power and wavelength shift in the process also limits the improvement of the detection limit. These features are difficult to avoid conflicting with the trends of convenience, stability, high speed, and low cost expected for biological detection.

Therefore, the biosensor system and the method for detecting the biological sample by using the same are developed, the technical defects of the existing method are overcome, the detection requirements of high speed, high efficiency, high accuracy and low cost are met, and the method has important practical significance.

Disclosure of Invention

The invention provides a biosensor system and a method for detecting a biological sample, aiming at the problems, the biosensor system is based on photonic integrated chip out-of-plane imaging, and simultaneously, the concentration of the biological sample is detected by taking image recognition analysis as a path, so that the technical defects of the existing method are overcome, and the biosensor system meets the detection requirements of rapidness, high efficiency and low cost.

In a first aspect of the present invention, there is provided a biosensor system comprising:

the sensor chip is provided with an optical waveguide and an on-chip micro-resonant cavity structure, and the optical waveguide and the on-chip micro-resonant cavity structure jointly form an optical coupling device, an optical conduction device and an optical resonance device;

a microfluidic channel integrated with the sensor chip;

and the imaging unit is used for acquiring scattering signals after optical coupling, optical conduction and optical resonance.

Preferably, the microfluidic channel is a Polydimethylsiloxane (PDMS) microfluidic channel.

Preferably, the method for integrating the sensor chip and the microfluidic channel is as follows:

adopting a silicon or photoresist structure with a photoetching defined pattern as a template, mixing basic components and a curing agent in Dow Corning SYLGARD184 in a container with a mold in a microfluidic channel, uniformly stirring, then placing in a vacuum box to remove bubbles, standing for curing, and finally stripping out the microfluidic channel;

the fluid input and output interfaces of the microfluidic channel are obtained through a puncher and are connected with an external fluid pump by inserting a metal connecting pipe;

under the assistance of optical microscope imaging, the sensor chip and the microfluidic channel are subjected to oxygen plasma treatment, so that irreversible bonding is formed on the surfaces of the sensor chip and the microfluidic channel, and the space alignment between the microfluidic channel with the micron scale and the sensor chip is obtained.

Preferably, the imaging unit includes any one of an objective lens and a microlens, and any one of a CMOS image sensor and a CCD image sensor.

Preferably, the imaging unit is an integrated device with a light source and a camera.

Preferably, the optical waveguide is a silicon nitride material.

Preferably, the sensor chip includes a plurality of sensing units, each of which has a micro-ring structure, and the micro-ring structure is any one of a circular type, a racetrack type, and a spiral type.

Preferably, the sensor chip further comprises a trunk optical waveguide and a multimode waveguide splitter for optical input of each sensing unit.

In a second aspect of the invention, there is provided a method of detecting a biological sample using a biosensor system as defined in any one of the above, the method comprising:

firstly, performing surface functionalization on an externally exposed part of a sensor chip, and fixing specific receptors aiming at multiple biomarkers in channels;

loading the sensor chip into an imaging unit, introducing light source exciting light into the main trunk waveguide, and simultaneously performing out-of-plane scattering imaging in the top view direction of the sensor chip;

introducing a solution to be detected by adopting a fluid pump;

acquiring real-time scattering signals by an imaging unit to obtain an out-of-plane scattering image;

and step five, analyzing the pixel matrix of the out-of-plane scattering image as input by using the established deep learning algorithm to obtain the real-time concentration change information of the object to be detected, and finally obtaining a dynamic analysis result.

Preferably, the method further comprises continuously introducing a solution labeled with specific antibody-linked metal nanoparticles for scattered signal amplification after introducing the solution to be tested using a fluid pump.

The invention provides a biosensor system, which comprises a sensor chip, a microfluidic channel and an imaging unit, and particularly relates to a method for integrating the sensor chip and the microfluidic channel, and the design of an optical waveguide and a micro resonant cavity structure. The beneficial effects are as follows:

1. compared with the traditional fluorescence-label-free optical biosensor system, the fluorescence-label-free optical biosensor system effectively avoids dependence on various optical precision measurements, requires low-cost and widely accessible equipment such as laser diodes and light emitting diodes, and greatly reduces the application of optical discrete devices. The biosensor system is highly suitable for integrated equipment, such as a smart phone, and can comprehensively use focusing laser and a camera of the smart phone and strong storage and operation capabilities of the smart phone, so that the whole sensor system is intelligent, streamlined and automatic. The sensor chip can be completed by using a CMOS adaptive wafer-level processing technology, so that the manufacturing cost is greatly reduced.

2. The invention is very suitable for multiplexing high-throughput measurement, such as screening of multiple markers in a complex body fluid environment, and does not need to add extra system components due to multi-channel detection by reasonably designing the format of the sensing device.

3. The invention has wide practicability, and various on-chip micro-resonant cavity structures, micro-interferometers and photonic crystal structures in the current field can be applied, so that information is read in a scattered signal monitoring mode, and sensing big data is fully utilized through an advanced deep learning algorithm. Iterative development of the back-end deep learning algorithm further reduces or eliminates part of noise sources, and compared with a traditional frequency domain detection scheme, the method achieves a more excellent detection limit.

Drawings

FIG. 1(a) is a schematic structural diagram of an on-chip-free coupling grating structure of a biosensor system according to an embodiment of the present invention; FIG. 1(b) is a schematic structural diagram of an on-chip coupling grating structure of a biosensor system according to an embodiment of the present invention;

FIG. 2(a) is a schematic cross-sectional view of a sensor chip according to an embodiment of the present invention; FIG. 2(b) is a schematic diagram of the effect of surface functionalization near an optical waveguide in a sensor chip according to an embodiment of the present invention; FIG. 2(c) is a schematic top view of three structures in the vicinity of an optical waveguide for locally introducing a scattering enhancement effect in an embodiment of the present invention;

FIG. 3 is a schematic diagram of four types of micro-ring structures of sensing units in a sensor chip according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of an overall top view of a multiplexed sensor chip according to an embodiment of the present invention;

FIG. 5 is a schematic flow chart of a method for detecting a biological sample by the biosensor system according to the embodiment of the present invention;

FIG. 6(a) is a schematic diagram of the spectrum of a biosensor system according to an embodiment of the present invention being changed by external adsorption disturbance during operation; FIG. 6(b) is a graph of the spectral response measured using the biosensor system in an embodiment of the present invention; FIG. 6(c) is a graph of a scatter image obtained using a biosensor system in an embodiment of the present invention;

FIG. 7(a) is a schematic diagram of a deep learning algorithm in an embodiment of the present invention; FIG. 7(b) is a diagram illustrating two exemplary measurements obtained by a deep learning algorithm in an embodiment of the present invention;

100, a sensor chip; 101. an on-chip micro-resonant cavity structure; 102. a microfluidic channel; 103. an objective lens or a microlens; 104. a CMOS or CCD image sensor; 105. an on-chip coupling grating structure; 106. an integration device; 107. a light source; 108. a camera; 109. a polymeric microfluidic channel layer; 110. body fluid or buffer solution to be tested; 111. an optical waveguide; 112. a cladding layer; 113. a base layer; 114. a monolayer; 115. a receptor; 116. an antigen to be detected; 117. a nano-metal particle; 118. a 1 × 2 beam splitter; 119. a 1 × 4 beam splitter; 120. a sensing unit and an independent microfluidic channel; 121 another set of sensing units and independent microfluidic channels.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The invention relates to a biosensor system, which is shown in figures 1-4 and comprises a sensor chip 100, a micro-fluidic channel 102 and two imaging units, wherein one imaging unit comprises an objective lens or a micro-lens 103 and a CMOS or CCD image sensor 104, the other imaging unit is an integrated device 106, the integrated device 106 is provided with a light source 107 and a camera 108 with a micro-distance imaging capability, the sensor chip 100 is provided with an on-chip coupling grating structure 105, an optical waveguide 111 and an on-chip micro-resonant cavity structure 101, and the on-chip coupling grating structure 105, the optical waveguide 111 and the on-chip micro-resonant cavity structure 101 form a light receiving, light coupling and light conducting device together; the sensor chip 100 is integrated with the microfluidic channel 102.

The sensor chip 100 is based on an on-chip passive planar optical waveguide loop, and utilizes capture and imaging of out-of-plane elastic scattering generated by light propagating in a waveguide to establish correlation between a pixel matrix and information of an object to be detected on the surface of the sensor, so that high-speed, accurate, simple, low-cost and reusable detection is realized. As shown in fig. 1(a), first, an end-face coupling is performed between the optical fiber and the main waveguide by using an off-chip lens, or as shown in fig. 1(b), an optical signal in the optical source is coupled to the main waveguide based on the auxiliary coupling of the periodic on-chip coupling grating structure 105, and then the input light is coupled from the main waveguide to the on-chip micro-resonant cavity structure 101 by evanescent coupling. Based on the inevitable sidewall roughness details or specific defect structures in the optical waveguide 111, part of the guided light beam interacts with the nanoscale structure to produce random, non-directional rayleigh scattering. In the out-of-plane direction perpendicular to the plane of the sensor chip 100, the far-field scattered signal acquisition can be performed in a focused manner using an objective lens or microlens 103, or in a lens-free, non-focused manner. With the continuous increase of the adsorption of the surface of the optical waveguide 111 under the surface functionalization to the marker to be detected in the fluid, the effective refractive index of the on-chip micro-resonant cavity structure 101 correspondingly increases, thereby changing the resonance condition. On the premise that the wavelength of the input light is fixed, the intensity and distribution of the far-field scattered light will be affected by the change of the self-optical-field limiting condition in the on-chip microresonator structure 101. Therefore, by analyzing the out-of-plane scattering image, the corresponding relation between the signal and the concentration of the object to be measured can be established, and the time resolution of sensing can be greatly improved by utilizing the advantage of high-speed imaging. Meanwhile, the accuracy can be improved in a self-checking mode by simultaneously photographing and measuring multiple units and multiple measurement groups in the single sensor chip 100, and multi-channel multiplexing detection for multiple biomarkers to be detected can also be realized. A large amount of data acquired by high-speed imaging can be used for training various deep learning technologies, and finally, the automatic, flow classification and analysis processes are realized.

As shown in fig. 2(a), there are a polymer microfluidic channel layer 109, a body fluid or buffer solution to be measured 110, a high refractive index optical waveguide 111, a low refractive index cladding 112, and a substrate layer 113. In a preferred embodiment of the present invention, the operative wavelength range that can be implemented covers a broad range (about 0.3-4 microns) from visible to mid-infrared for on-chip photonic circuits, with a preferred operative wavelength range of 600-1000 nm. Based on the waveband, the CMOS or CCD image sensor 104 with low cost and high sensitivity is adopted, and can be seamlessly adapted to the integrated device 106, such as a personal device like a smart phone. The optical waveguide and microresonator structure 101 on the sensor chip 100 is implemented primarily based on a mature top-down chip micromachining process, preferably a silicon-based substrate, and the preparation of the photonic loop portion can be implemented using a mature CMOS compatible semiconductor processing process.

The optical waveguide 111 is preferably a silicon nitride material having an extremely wide transparent window ranging from visible to infrared, and can obtain an extremely low loss coefficient within a specific operating wavelength by adjusting the specific gravities of silicon and nitrogen elements in chemical vapor deposition. In addition, the silicon nitride material has a thermo-optic coefficient far lower than that of silicon, which is beneficial to reducing signal noise generated by chip heat generation. Other preferred solutions include silicon waveguide devices formed on silicon-on-insulator (SOI) chips, high index glass waveguide devices formed by ion implantation on glass or quartz substrates, and a variety of polymer waveguide devices, preferably SU8, PMMA, etc. The processing flows mainly related to the various platforms comprise photoetching or electron beam exposure, dry etching and the like.

In a preferred embodiment of the present invention, the microfluidic channel 102 is a Polydimethylsiloxane (PDMS) microfluidic channel. The integration of the sensor chip 100 with the microfluidic channel 102 mainly involves combining a PDMS based microfluidic channel with the sensor chip 100, using a lithographically defined pattern of silicon or photoresist structures as a template, the PDMS channel can use the basic components and curing agents in dow corning SYLGARD184, mixed in a container with a mold placed, stirred evenly, then placed in a vacuum box to remove air bubbles, left to cure, and finally peeled off. The fluid input and output interfaces can be obtained by a puncher and inserted into a metal connecting pipe to be connected with an external fluid pump, and then the sensor chip 100 and the PDMS are placed in oxygen plasma treatment to form irreversible bonding on the two surfaces, and the process can be carried out with the help of optical microscope imaging, so that good space alignment can be obtained between the micro-fluidic channel 102 with the micron scale and the sensor chip 100.

In the preferred embodiment of the invention, for the fluorescence-label-free biosensing, the surface functionalization needs to add a chemical modifier layer, a cross-linking agent layer and a biological monolayer on the surface of the optical waveguide. The optical waveguide is a silicon nitride material, and the preparation method for adding the chemical modifier layer, the cross-linking agent layer and the biological monomolecular layer on the surface of the optical waveguide is as follows: organosilane having various functional groups such as amino group (-NH2), carboxyl group (-COOH) and thiol (-SH) are used for binding of protein, enzyme or nucleic acid by using a natural silica passivation layer or depositing a silica layer of about 5nm, preferably APTES as a modifier layer and EDC/NHS as a cross-linker layer, by increasing the surface hydroxyl group density through piranha solution or oxygen plasma treatment, followed by a surface silanization process. For a multi-channel sensing system, each microfluidic channel can individually perform specific surface functionalization. In actual test, the solution to be tested can be body fluid (such as serum, saliva and the like) or prepared Phosphate Buffered Saline (PBS), and the specific marker in the solution and the surface receptor realize high interaction force due to strong structural complementarity and affinity to obtain specific binding, so as to generate disturbance to an evanescent wave optical field. After the adsorption of the to-be-detected marker is completed, the secondary signal amplification step may also be performed using the metal nanoparticles 117 to which a specific receptor is attached. As shown in fig. 2(b), the self-assembled monolayer 114 formed after surface silanization, the receptor 115 after immobilization, the antigen 116 to be detected, and the nano-metal particles 117 with specific antibodies bound to the surface for signal amplification are respectively shown.

In a preferred embodiment of the present invention, as shown in FIG. 2(c), the intrinsic optical field signal in the optical waveguide or microresonator structure, and the change in the optical field distribution due to the test object, can be indirectly measured by the scattered signal. Random scattering can be caused by the inevitable rough shape of the side wall caused by the processes of photoetching exposure, etching and the like, and a small amount of concave-convex structures can be introduced into a local area to serve as defects so as to enhance the local scattering effect at fixed points. The defect design is not too much or too large, otherwise the optical loss is greatly increased, the quality factor of optical resonance is reduced, and the sensing performance is influenced.

In a preferred embodiment of the present invention, the sensor chip includes a plurality of sensing units, each of which has a micro-ring structure, wherein the micro-ring structure is any one of a circular type, a racetrack type and a spiral type, and as shown in fig. 3, in order to avoid bending loss of the optical waveguide and to allow for miniaturization, the radius of the micro-ring of the sensing unit is typically 10-100 μm. In addition, a slot-mode micro-ring design can be selected to enhance the specific gravity of evanescent waves and improve the sensitivity. In addition, the double-micro-ring coupling design can also be adopted, due to the size difference of the two micro-ring structures, the resonance modes with different orders can be selectively coupled, the self-calibration of a part of weak coupling efficiency mode serving as a strong coupling efficiency mode is realized, and the better noise immunity effect is realized.

In the preferred embodiment of the present invention, a group of sensors for sensing a single biomarker comprises a single or multiple micro-ring sensing units and independent microfluidic channels, a group of sensors comprising multiple sensing units, preferably 2-4, can be provided with one unit for calibration (i.e. an upper cladding layer is left and does not interact with an analyte in a solution), and the remaining multiple devices can be used for multiple measurements in real time, so as to reduce the measurement signal error.

Further, the sensor chip further comprises a main optical waveguide and a multimode waveguide beam splitter, which are used for optical input of each sensing unit, and the sensor chip can integrate multiple groups of sensors, preferably 8 or 16 groups, so as to cooperate with multiple groups of microfluidic channels to realize simultaneous measurement facing to multiple different biomarkers. The light input for each set of sensors is accomplished by a trunk waveguide and multimode waveguide beam splitter, shown in fig. 4 as on-chip 1 × 2 beam splitter 118; the multi-mode interference waveguide-based on-chip 1 × 4 beam splitter 119, a sensing unit and an independent microfluidic channel 120 based on four micro-ring structures, and another group of sensing units and independent microfluidic channels 121 based on four micro-ring structures.

The method for detecting a biological sample by using the biosensor system according to any one of the above embodiments, as shown in fig. 5, includes the following steps:

firstly, performing surface functionalization on an externally exposed part of a sensor chip, and fixing specific receptors aiming at multiple biomarkers in channels;

loading the sensor chip into an imaging unit, introducing light source exciting light into the main trunk waveguide, and simultaneously performing out-of-plane scattering imaging in the top view direction of the sensor chip;

introducing a solution to be detected by adopting a fluid pump;

acquiring real-time scattering signals by an imaging unit to obtain an out-of-plane scattering image;

and step five, analyzing the pixel matrix of the out-of-plane scattering image as input by using the established deep learning algorithm to obtain real-time concentration change information of the object to be detected and finally obtain a dynamic analysis result, specifically as shown in fig. 5, analyzing the pixel matrix based on a training algorithm, classifying signal attributes, judging dynamic concentration change and performing dynamic analysis.

Preferably, the method further comprises continuously introducing a solution labeled with specific antibody-linked metal nanoparticles for scattered signal amplification after introducing the solution to be tested using a fluid pump.

In a specific implementation process, the scattering imaging is mainly collection of scattered light in an out-of-plane vertical direction, and as shown in fig. 1(a) and 1(b), the scattering imaging can be based on three modes, namely a common optical objective lens, a miniaturized polymer objective lens and lens-free imaging. After the proper numerical aperture of the objective lens or the micro lens and the size of the camera sensor are determined, the field of view (FOV) is ensured to cover all the sensing units, and the measurement of all the devices can be completed through single-exposure imaging. The biosensor system may be based on a light source with a fixed wavelength, separate from the chip, which may be a Laser Diode (LD), a superluminescent light emitting diode (SLD), or a Light Emitting Diode (LED) with a narrower line width. As shown in fig. 6(a), due to the disturbance of the adsorption of the analyte to the formant, the intensity of the transmitted signal of the light wave with the fixed excitation wavelength and the enhancement effect in the cavity change, thereby affecting the intensity and distribution of the scattering signal. Fig. 6(b) is an experimental record of the change in transmission spectrum of a single silicon nitride microring that has been implemented. Fig. 6(c) reflects the corresponding scatter imaged image. A significant change in the scattering signal is observed after adsorption of a particular protein corresponding to a fixed operating wavelength a. Whereas at a fixed wavelength B it can be observed that the scattered signal becomes very weak as it deviates from the range of the formants.

In the data processing of the obtained internal and external scatter images, a two-dimensional pixel matrix of preferably 50 × 50 can be formed for a single cell, or a simplified one-dimensional pixel matrix with a greatly reduced number of pixels can be formed by selecting a specific defective region, as shown in fig. 7 (a). Besides the scattering imaging of the microring, the scattering pixel matrix of the input waveguide and the output waveguide can also be used as a reference factor to be incorporated into the analysis process.

In a preferred embodiment of the invention, the scatter image analysis is preferably a flow detection dominated by a deep learning algorithm. Mainly related to data training in response to image changes of the sensor system during preparation. The preferred algorithm is a convolutional neural network, which, as shown in fig. 7(a), comprises multiple convolutional and pooling layers, and finally sends the output values to the classifier in a fully-connected layer.

A preferred learning process includes introducing fluid samples of different concentrations, such as saline, glucose solution, index matching oil, etc., collecting scatter images of the underlying band with different indices of refraction, and learning the response. Hundreds of images can be collected for each concentration sample for training and hundreds of images for verification. Preferably, a wavelength tunable laser is used for frequency sweeping input, a plurality of groups of hyperspectral images are formed, and the hyperspectral images are substituted for training and verification.

Furthermore, a calibration experiment with known concentrations of multiple markers should be performed on one sensor chip to establish a correlation coefficient between the optical signal and the actual concentration value.

After the training and verification process is completed, a real solution to be tested is introduced, a convolutional neural network algorithm is applied, and the real-time output result in the whole sensing stage (which can be from minutes to hours) in reference to fig. 7(b) comprises two types of confusion matrix judgment and time domain classification:

one is to determine whether the response is normal, a response that is expected, whether an abnormal response exists, and whether a locally altered response (e.g., local scattering enhancement by a single metal nanoparticle) exists. By adjusting the determination threshold, the situation of interference due to partial nonspecific response and local impurities can be eliminated and considered as abnormal response. In addition, a normal response is assumed.

And secondly, judging the real-time surface adsorption amount, deducing the surface adsorption amount of each sensing unit by a specific image at a specific time through the correlation between the image change degree and the resonance red shift intensity established in the training process, namely the real-time concentration change information of the object to be detected, establishing the monitoring of the dynamic process, and finally, comprehensively analyzing to obtain the affinity coefficient of the intermolecular interaction when the process is close to a steady state, namely the dynamic analysis result.

The invention provides a novel efficient, flow-based and real-time tracking working mechanism for an on-chip optical sensor and provides a novel contact mechanism which is used for establishing a path between a mode response and information of an object to be detected and takes image recognition analysis as the path. The sensor chip based on the on-chip passive planar optical waveguide loop utilizes capture and imaging of out-of-plane elastic scattering generated by light transmitted in the waveguide to establish correlation between a pixel matrix and information of an object to be detected on the surface of the sensor, realizes a detection scheme with high speed, accuracy, simplicity, convenience, low cost and reusability, can establish a corresponding relation between a signal and the concentration of the object to be detected by analyzing an out-of-plane scattering image, and can greatly improve the time resolution of sensing by utilizing the advantage of high-speed imaging. Meanwhile, the multi-unit and multi-measurement group in the single chip are photographed and measured simultaneously, the accuracy can be improved in a self-checking mode, and multi-channel multiplexing detection for various to-be-detected biomarkers can be realized. A large amount of data acquired by high-speed imaging can be used for training various deep learning technologies, and finally, the automatic, flow classification and analysis processes are realized.

In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process or method.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.