阅读说明：本技术 一种电路的检测方法、装置、电子设备及可读存储介质 (Circuit detection method and device, electronic equipment and readable storage medium ) 是由 曾健忠 于 2021-08-03 设计创作，主要内容包括：本申请适用于电路技术领域,提供了一种电路的检测方法、装置、电子设备及可读存储介质,方法包括：获取目标电路的第一性能参数,第一性能参数为对目标电路进行预设低级别次数的蒙地卡罗模拟,得到目标电路的模拟性能参数,预设低级别次数小于预设高良率要求进行蒙地卡罗模拟对应的预设高级别次数；或者,第一性能参数为对目标电路进行测试得到的真实性能参数；对第一性能参数进行正态性转换,得到进行正态性转换后的第二性能参数；在第二性能参数中,确定与预设西格玛范围匹配的第三性能参数；将第三性能参数进行正态性反转换,得到第四性能参数；根据第四性能参数,确定目标电路的良率。可通过较少的模拟时间精确的确定出目标电路的良率。(The application is applicable to the technical field of circuits, and provides a circuit detection method, a circuit detection device, electronic equipment and a readable storage medium, wherein the method comprises the following steps: acquiring a first performance parameter of a target circuit, wherein the first performance parameter is obtained by carrying out Monte Carlo simulation on the target circuit for preset low-level times to obtain a simulation performance parameter of the target circuit, and the preset low-level times are smaller than preset high-level times corresponding to Monte Carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit; performing normality conversion on the first performance parameter to obtain a second performance parameter subjected to the normality conversion; determining a third performance parameter matched with the preset sigma range in the second performance parameters; performing normal reverse conversion on the third performance parameter to obtain a fourth performance parameter; and determining the yield of the target circuit according to the fourth performance parameter. The yield of the target circuit can be accurately determined through less simulation time.)

1. A method of testing a circuit, comprising:

acquiring a first performance parameter of a target circuit, wherein the first performance parameter is obtained by carrying out Monte Carlo simulation on the performance parameter of the target circuit for preset low-level times to obtain a simulated performance parameter of the target circuit, and the preset low-level times are smaller than preset high-level times corresponding to Monte Carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit;

performing normality conversion on the first performance parameter to obtain a second performance parameter subjected to the normality conversion;

determining a third performance parameter matched with a preset sigma range in the second performance parameters;

performing normal reverse conversion on the third performance parameter to obtain a fourth performance parameter;

and determining the yield of the target circuit according to the fourth performance parameter.

2. The method for detecting a circuit according to claim 1, wherein performing a normality conversion on the first performance parameter to obtain a second performance parameter after performing the normality conversion includes:

and performing normality conversion on the first performance parameter according to the normalization conversion of the BOXCOX to obtain a second performance parameter subjected to the normality conversion.

3. The method according to claim 2, wherein the performing a normality conversion on the first performance parameter according to a BOXCOX normality conversion to obtain a calculation formula of a second performance parameter after the performing the normality conversion comprises:

wherein, the x_boxcoxRepresenting the second performance parameter after normal conversion, x_IcellAnd expressing a first performance parameter, wherein lambda is a preset adjustable parameter.

4. The method of claim 1, wherein the determining the yield of the target circuit according to the fourth performance parameter comprises:

performing Monte Carlo simulation on the fourth performance parameter for preset high-level times to obtain a target yield value corresponding to the target sigma value;

searching a standard yield value corresponding to the target sigma value in a preset relation mapping table;

and obtaining a target circuit yield analysis result according to the relation between the standard yield value and the target yield value.

5. The method of claim 4, wherein performing a predetermined high-level number of Monte Carlo simulations on the fourth performance parameter to obtain a target yield value corresponding to a target sigma value comprises:

performing Monte Carlo simulation on the fourth performance parameter for preset high-level times to obtain a fifth performance parameter corresponding to the yield analysis;

determining a normal distribution parameter of the fifth performance parameter, the normal distribution parameter comprising a plurality of sigma values and corresponding probability values;

and determining a probability value corresponding to a target sigma value as the target yield value according to the normal distribution parameter.

6. The method for testing a circuit according to claim 1, wherein said determining a third performance parameter matching a preset sigma range among said second performance parameters comprises:

dividing the second performance parameter based on normal distribution;

and determining the second performance parameter belonging to the preset sigma range as a third performance parameter matched with the preset sigma according to the distribution state of the second performance parameter after normal distribution division.

7. The method of any one of claims 1 to 6, wherein the target circuit comprises a static memory cell, and the performance parameter of the target circuit comprises a current value of the static memory cell.

8. A circuit testing apparatus, comprising:

the circuit comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a first performance parameter of a target circuit, the first performance parameter is obtained by carrying out Monte Carlo simulation on the performance parameter of the target circuit for preset low-level times, and the preset low-level times are smaller than preset high-level times corresponding to Monte Carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit;

the normal conversion module is used for carrying out normal conversion on the first performance parameter to obtain a second performance parameter after the normal conversion;

the first determining module is used for determining a third performance parameter matched with a preset sigma range in the second performance parameters;

the normal inverse transformation module is used for performing normal inverse transformation on the third performance parameter to obtain a fourth performance parameter;

and the second determining module is used for determining the yield of the target circuit according to the fourth performance parameter.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.

Technical Field

The present application relates to the field of circuit technologies, and in particular, to a method and an apparatus for detecting a circuit, an electronic device, and a readable storage medium.

Background

With the process size of the chip being smaller and smaller, the integration level of the circuit is higher and higher, the requirement for the yield detection of the circuit is higher and higher, the higher the yield of the product is, the probability of detecting an abnormal event is smaller and smaller, and the high yield requirement can be met only by detecting a large amount of data. High yield detection can be achieved through Monte Carlo simulation, but the required Monte Carlo simulation times are too large, and the detection efficiency is low.

Disclosure of Invention

The embodiment of the application provides a circuit detection method and device, electronic equipment and a readable storage medium, and aims to solve the problem that the existing circuit detection method with high yield is low in efficiency.

In a first aspect, an embodiment of the present application provides a method for detecting a circuit, including:

acquiring a first performance parameter of a target circuit, wherein the first performance parameter is obtained by carrying out Monte Carlo simulation on the performance parameter of the target circuit for preset low-level times to obtain a simulated performance parameter of the target circuit, and the preset low-level times are smaller than preset high-level times corresponding to Monte Carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit;

performing normality conversion on the first performance parameter to obtain a second performance parameter subjected to the normality conversion;

determining a third performance parameter matched with a preset sigma range in the second performance parameters;

performing normal reverse conversion on the third performance parameter to obtain a fourth performance parameter;

and determining the yield of the target circuit according to the fourth performance parameter.

In a second aspect, an embodiment of the present application provides a detection apparatus for a circuit, including:

the circuit comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a first performance parameter of a target circuit, the first performance parameter is obtained by carrying out Monte Carlo simulation on the performance parameter of the target circuit for preset low-level times, and the preset low-level times are smaller than preset high-level times corresponding to Monte Carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit;

the normal conversion module is used for carrying out normal conversion on the first performance parameter to obtain a second performance parameter after the normal conversion;

the first determining module is used for determining a third performance parameter matched with a preset sigma range in the second performance parameters;

the normal inverse transformation module is used for performing normal inverse transformation on the third performance parameter to obtain a fourth performance parameter;

and the second determining module is used for determining the yield of the target circuit according to the fourth performance parameter.

In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the detection method of the above circuit when executing the computer program.

In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the detection method of the above circuit are implemented.

In a fifth aspect, the present application provides a computer program product, which when run on an electronic device, causes the electronic device to perform the steps of the detection method of the above-mentioned circuit.

Compared with the prior art, the embodiment of the application has the advantages that: the method comprises the steps that a first performance parameter of a target circuit can be obtained, the first performance parameter is the Monte Carlo simulation of the performance parameter of the target circuit for preset low-level times, the simulation performance parameter of the target circuit is obtained, and the preset low-level times are smaller than the preset high-level times corresponding to Monte Carlo simulation required by preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit; performing normality conversion on the first performance parameter to obtain a second performance parameter subjected to the normality conversion; determining a third performance parameter matched with a preset sigma range in the second performance parameters; performing normal reverse conversion on the third performance parameter to obtain a fourth performance parameter; and determining the yield of the target circuit according to the fourth performance parameter. The normal distribution of the first performance parameter and the standard normal distribution usually have offset, so that the first performance parameter can be subjected to normal conversion to obtain more accurate normal distribution, the second performance parameter subjected to the normal conversion is obtained, the third performance parameter matched with the preset sigma range is determined in the second performance parameter, the yield of the target circuit is determined only according to the fourth performance parameter, and the fourth performance parameter is based on the performance parameter matched with the preset sigma range after the normal conversion, so that the acquisition probability of failure parameters can be improved, namely the performance parameter which does not meet the requirement can be easily acquired, and the yield of the target circuit can be accurately determined through less simulation time.

It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a schematic flow chart illustrating a method for detecting a circuit according to an embodiment of the present disclosure;

FIG. 2 is a graph of a normal distribution of a performance parameter of a target circuit according to an embodiment of the present application;

FIG. 3 is a normal distribution plot of yet another performance parameter of a target circuit provided by an embodiment of the present application;

FIG. 4 is a circuit diagram of a static memory cell according to an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating a process for performing a normality conversion according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a comparison of normalization transfer curves provided by an embodiment of the present application;

FIG. 7 is a table diagram illustrating parameters associated with performing a normal inversion according to an embodiment of the present application;

fig. 8 is a schematic flowchart of step S105 according to an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a detection apparatus of a circuit provided in an embodiment of the present application;

fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".

Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

The embodiment of the application provides a circuit detection method which can be particularly applied to the fields of circuit detection, circuit simulation, circuit design and the like. The method and the device can also be applied to electronic devices such as simulation equipment, upper computers, mobile phones, tablet computers, Augmented Reality (AR)/Virtual Reality (VR) equipment, notebook computers, ultra-mobile personal computers (UMPCs), netbooks, Personal Digital Assistants (PDAs), and the like, and the embodiment of the present application does not limit the specific types of the electronic devices at all.

In order to explain the technical means described in the present application, the following examples are given below.

Referring to fig. 1, a method for detecting a circuit according to an embodiment of the present application includes:

step S101, acquiring a first performance parameter of a target circuit, wherein the first performance parameter is obtained by carrying out Monte Carlo simulation on the performance parameter of the target circuit for preset low-level times to obtain a simulated performance parameter of the target circuit, and the preset low-level times are smaller than preset high-level times corresponding to Monte Carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit.

Specifically, the target circuit may be a circuit that needs to be subjected to performance analysis, such as a Static Random-Access Memory (SRAM) circuit, and the obtaining of the first performance parameter of the target circuit may be performing monte carlo simulation on the target circuit for a preset number of times at a low level to obtain a simulated performance parameter of the target circuit; the preset low-level times are less than the preset high-level times corresponding to Monte Carlo simulation required by the preset high yield; the preset number of low levels may be preset, for example, several thousand times, for example, 3000 times, and this is only an example, and is not limited according to the actual application setting. The preset high-level times may be in the order of tens of thousands of times, or millions of times. Or, the first performance parameter is a real performance parameter obtained by performing a Wafer Acceptance Test (WAT) on the target circuit.

In one embodiment, the target circuit includes a static memory cell, and the performance parameter of the target circuit includes a current value (Icell) of the static memory cell.

In one application, the performance parameter of the target circuit is subjected to Monte Carlo simulation for a preset low-level number of times to obtain a distribution diagram of a first performance parameter of the target circuit, as shown in FIG. 2, which is a normal distribution diagram of a current value (Icell) parameter of a static memory cell in the target circuit, and it can be seen from FIG. 2 that the obtained normal distribution diagram is a distribution that is shifted to the left compared with a standard normal distribution (0, 1).

In one embodiment, the performance parameter of the target circuit may further include a voltage value (Vts) of the PD-static memory cell or a voltage value (Vts) of the PG-static memory cell, such as the static memory cell being a PD-static memory cell (pull-down type static memory cell) or a PG-static memory cell (through gate type static memory cell). As shown in fig. 3, includes a distribution curve 31 of the current value (Icell) of the static memory cell, a distribution curve 32 of the voltage value (Vts) of the PD-static memory cell, and a distribution curve 33 of the voltage value (Vts) of the PG-static memory cell, respectively. It can be seen from fig. 3 that the resulting parametric distribution curves all exhibit a distribution shifted to the left compared to the standard normal distribution.

In one embodiment, as shown in fig. 4, the circuit structure of a PD-static memory cell, the static memory cell of a 6T structure, which includes six transistors (N1, N2, N3, N4, P1, and P2), a Word Line (WL), a bit line (BL/BLB), and the like, can implement read and write operations of storing information.

And S102, performing the normality conversion on the first performance parameter to obtain a second performance parameter after the normality conversion.

Specifically, the performance parameter of the target circuit is usually subjected to monte carlo simulations for a preset low-level number of times, and the distribution curve of the first performance parameter of the target circuit is a curve having an offset compared with a standard normal distribution, and is a distribution shifted to the left compared with the standard normal distribution (also referred to as gaussian distribution) as shown in fig. 2. Because the distribution is not a standard normal distribution, the sigma of the standard normal distribution is used for analyzing the yield, and the error rate is often high, the first performance parameter is subjected to the normal conversion to obtain the second performance parameter subjected to the normal conversion.

In one embodiment, the performing the normality conversion on the first performance parameter to obtain the second performance parameter after the normalization conversion includes: and performing normality conversion on the first performance parameter according to the normalization conversion of the BOXCOX to obtain a second performance parameter subjected to the normality conversion.

In an embodiment, the performing an normality conversion on the first performance parameter according to a BOXCOX normality conversion to obtain a calculation formula of a second performance parameter after the performing the normality conversion includes:

wherein, the x_boxcoxRepresenting the second performance parameter after normal conversion, x_IcellAnd expressing a first performance parameter, wherein lambda is a preset adjustable parameter.

Specifically, a suitable λ is selected, and the first performance parameter can satisfy all assumed conditions of the normal regression model through the BOXCOX normality transformation, where the λ can be calculated in advance based on a maximum likelihood estimation algorithm, for example, where the predetermined λ is 1.165.

In a specific application, as shown in fig. 5, in order to perform the normal transformation on the curve 51 corresponding to the first performance parameter to obtain a schematic diagram of the curve 52 corresponding to the second performance parameter after the normal transformation, it can be seen that the curve with the shifted first performance parameter is changed into the curve with the second performance parameter conforming to the standard normal distribution.

And step S103, determining a third performance parameter matched with a preset sigma range in the second performance parameters.

Specifically, the preset sigma range may be preset according to a required yield level, and when the required yield level is higher, the set preset sigma is larger, and the second performance parameter beyond the preset sigma range is determined as the third performance parameter. The failed performance parameter is more easily obtained in the second performance parameters beyond the preset sigma range.

In one embodiment, it can be understood that the effective performance parameter in the high-yield application scenario is much larger than the failure performance parameter, so that the failure performance parameter is a small-probability event, and in a scenario with a higher yield level, the probability event of the failure performance parameter is rare, so that hundreds of thousands or millions of levels of simulation are required to obtain the failure performance parameter, and therefore the second performance parameter corresponding to the failure performance parameter exceeding the preset sigma range is determined as the third performance parameter, and the third performance parameter corresponding to the failure performance parameter exceeding the preset sigma range is a small-probability event, so that the obtaining probability of the failure parameter is easier to increase in the third performance parameter, that is, the performance parameter which does not meet the requirement is easier to obtain, and thus the yield of the target circuit can be accurately determined through less simulation time.

In an application scenario, as shown in fig. 6, a curve 61 is a distribution curve corresponding to a first performance parameter, a curve 62 is a curve corresponding to a second performance parameter, and if a third performance parameter determined correspondingly to exceed a preset sigma range is taken as the first performance parameter, the accuracy of determining the small-probability event is much lower than that of determining the small-probability event using the first performance parameter conforming to the standard normal distribution.

In one embodiment, the determining, among the second performance parameters, a third performance parameter matching a preset sigma range includes: dividing the second performance parameter based on normal distribution; and determining the second performance parameter belonging to the preset sigma range as a third performance parameter matched with the preset sigma according to the distribution state of the second performance parameter after normal distribution division.

Specifically, on the basis of normal distribution, by performing normal distribution fitting on the second performance parameters, the performance parameters exceeding the preset sigma range in the second performance parameters are determined as third performance parameters matched with the preset sigma.

And step S104, performing normal reverse conversion on the third performance parameter to obtain a fourth performance parameter.

Specifically, since the third performance parameter is not the original performance parameter, the third performance parameter may be subjected to a normalization reverse transformation, and the normalization reverse transformation may perform one-to-one mapping transformation on the corresponding data, so as to obtain a corresponding fourth performance parameter.

In an application scenario, as shown in fig. 7, a related data correspondence table of a fourth performance parameter is exemplarily shown, where the third and fourth columns are a standard deviation and a corresponding probability value in a normal distribution curve corresponding to the third performance parameter, and the first and second columns are subjected to a normal inverse transformation to obtain a standard deviation and a corresponding probability value in a normal distribution curve corresponding to the fourth performance parameter.

Step S105, determining a yield of the target circuit according to the fourth performance parameter.

Specifically, a fourth performance parameter corresponding to the third performance parameter is subjected to high yield rate grade times simulation through Monte Carlo simulation, a failure performance parameter is determined according to a simulation result, the failure performance parameter is divided by the simulated total performance parameter, the yield of the target circuit can be determined, and then the yield is compared with a preset standard yield to judge whether the target circuit is qualified or unqualified.

In one embodiment, as shown in fig. 8, step S105 includes steps S1051 to S1053:

step S1051, performing monte carlo simulation of preset high-level times on the fourth performance parameter to obtain a target yield value corresponding to the target sigma value.

In one embodiment, performing a preset high-level number of monte carlo simulations on the fourth performance parameter to obtain a target yield value corresponding to a target sigma value, includes: performing Monte Carlo simulation on the fourth performance parameter for preset high-level times to obtain a fifth performance parameter corresponding to the yield analysis; determining a normal distribution parameter of the fifth performance parameter, the normal distribution parameter comprising a plurality of sigma values and corresponding probability values; and determining a probability value corresponding to a target sigma value as the target yield value according to the normal distribution parameter.

Specifically, Monte Carlo simulation of preset high-level times is performed on the fourth performance parameter to obtain fifth simulation data, normal distribution simulation is performed according to the fifth simulation data, and a target yield value corresponding to the target sigma value can be determined according to the simulated normal distribution.

Step S1052, finding a standard yield value corresponding to the target sigma value in a preset mapping table.

Specifically, the preset map includes information of a plurality of sigma values corresponding to the standard yield values and the number of times of simulation, and the standard yield value corresponding to the target sigma value can be searched in the preset map.

Step S1053, obtaining the target circuit yield analysis result according to the relation between the standard yield value and the target yield value.

Specifically, the target circuit yield analysis result is obtained according to the relationship between the standard yield value and the target yield value. If the absolute value of the difference between the target yield value and the preset standard yield is smaller than a preset threshold value, judging that the target circuit is qualified; and if the difference value between the target yield value and the preset standard yield is greater than or equal to a preset threshold value, determining that the target circuit is unqualified.

According to the embodiment of the application, due to the fact that offset usually exists between the normal distribution and the standard normal distribution of the first performance parameter, the first performance parameter can be subjected to normal conversion to obtain more accurate normal distribution, the second performance parameter after the normal conversion is obtained, the third performance parameter matched with the preset sigma range is determined in the second performance parameter, the yield of the target circuit is determined only according to the fourth performance parameter, and the fourth performance parameter is based on the performance parameter matched with the preset sigma range after the normal conversion, so that the obtaining probability of the failure parameter can be improved, namely, the performance parameter which does not meet requirements can be obtained easily, and the yield of the target circuit can be determined accurately through less simulation time.

The embodiment of the present application further provides a detection apparatus for a circuit, and as shown in fig. 9, the detection apparatus for a circuit in the embodiment of the present application is used to execute the steps in the embodiment of the detection method for a circuit. The circuit detection apparatus may be a virtual appliance (virtual application) in the electronic device, which is executed by a processor of the electronic device, or may be the electronic device itself, and the circuit detection apparatus 900 includes:

an obtaining module 901, configured to obtain a first performance parameter of a target circuit, where the first performance parameter is a simulated performance parameter of the target circuit obtained by performing monte carlo simulation on the performance parameter of the target circuit for a preset low-level number of times, and the preset low-level number of times is smaller than a preset high-level number of times corresponding to the execution of the monte carlo simulation required by a preset high yield; or the first performance parameter is a real performance parameter obtained by testing the target circuit;

a normal conversion module 902, configured to perform a normality conversion on the first performance parameter to obtain a second performance parameter after the normality conversion is performed;

a first determining module 903, configured to determine, in the second performance parameters, a third performance parameter that matches a preset sigma range;

a normal inverse transformation module 904, configured to perform a normal inverse transformation on the third performance parameter to obtain a fourth performance parameter;

a second determining module 905, configured to determine a yield of the target circuit according to the fourth performance parameter.

In one embodiment, the normal conversion module 902 is specifically configured to: and performing normality conversion on the first performance parameter according to the normalization conversion of the BOXCOX to obtain a second performance parameter subjected to the normality conversion.

In an embodiment, the performing an normality conversion on the first performance parameter according to a BOXCOX normality conversion to obtain a calculation formula of a second performance parameter after the performing the normality conversion includes:

wherein, the x_boxcoxRepresenting the second performance parameter after normal conversion, x_IcellAnd expressing a first performance parameter, wherein lambda is a preset adjustable parameter.

In one embodiment, the second determination module 905 includes:

the simulation unit is used for carrying out Monte Carlo simulation on the fourth performance parameter for preset high-level times to obtain a target yield value corresponding to a target sigma value;

the searching unit is used for searching a standard yield value corresponding to the target sigma value in a preset relational mapping table;

and the obtaining unit is used for obtaining the target circuit yield analysis result according to the relation between the standard yield value and the target yield value.

In one embodiment, the analog unit is specifically configured to: performing Monte Carlo simulation on the fourth performance parameter for preset high-level times to obtain a fifth performance parameter corresponding to the yield analysis; determining a normal distribution parameter of the fifth performance parameter, the normal distribution parameter comprising a plurality of sigma values and corresponding probability values; and determining a probability value corresponding to a target sigma value as the target yield value according to the normal distribution parameter.

In one embodiment, the first determining module 903 is specifically configured to: dividing the second performance parameter based on normal distribution; and determining the second performance parameter belonging to the preset sigma range as a third performance parameter matched with the preset sigma according to the distribution state of the second performance parameter after normal distribution division.

In one embodiment, the target circuit includes a static memory cell, and the performance parameter of the target circuit includes a current value of the static memory cell.

According to the embodiment of the application, due to the fact that offset usually exists between the normal distribution and the standard normal distribution of the first performance parameter, the first performance parameter can be subjected to normal conversion to obtain more accurate normal distribution, the second performance parameter after the normal conversion is obtained, the third performance parameter matched with the preset sigma range is determined in the second performance parameter, the yield of the target circuit is determined only according to the fourth performance parameter, and the fourth performance parameter is based on the performance parameter matched with the preset sigma range after the normal conversion, so that the obtaining probability of the failure parameter can be improved, namely, the performance parameter which does not meet requirements can be obtained easily, and the yield of the target circuit can be determined accurately through less simulation time.

As shown in fig. 10, an embodiment of the present invention further provides an electronic device 100 including: a processor 1001, a memory 1002 and a computer program 1003, such as a detection program for an electric circuit, stored in said memory 1002 and executable on said processor 1001. The processor 1001, when executing the computer program 1003, implements the steps in the above-described embodiments of the detection method. The processor 1001, when executing the computer program 1003, implements the functions of the modules in the device embodiments, for example, the functions of the modules 901 to 905 shown in fig. 9.

Illustratively, the computer program 1003 may be divided into one or more modules, which are stored in the memory 1002 and executed by the processor 1001 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 1003 in the electronic device 100. For example, the computer program 1003 may be divided into a first simulation module, a normal conversion module, a first determination module, a normal inverse conversion module, and a second determination module, and specific functions of the modules are described in the foregoing embodiments, and are not described herein again.

The electronic device 100 may be an electronic device such as a simulation device, an upper computer, a notebook, a palm computer, and a server. The electronic device may include, but is not limited to, a processor 1001, a memory 1002. Those skilled in the art will appreciate that fig. 10 is merely an example of the electronic device 100 and does not constitute a limitation of the electronic device 100 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.

The Processor 1001 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 1002 may be an internal storage unit of the electronic device 100, such as a hard disk or a memory of the electronic device 100. The memory 1002 may also be an external storage device of the electronic device 100, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the electronic device 100. Further, the memory 1002 may also include both an internal storage unit and an external storage device of the electronic device 100. The memory 1002 is used for storing the computer programs and other programs and data required by the electronic device. The memory 1002 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated module, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.