Optimization method and optimization device of test case set and storage medium

Abstract

An optimization method and an optimization device for a test case set, the optimization method comprising: obtaining a Pcell and CDF parameter information of the Pcell, the Pcell having a plurality of parameter modules, each parameter module having at least one CDF parameter, the parameter information of the Pcell being suitable for representing the function of each parameter module and the function association between different parameter modules; obtaining a to-be-optimized test case set; and according to the Pcell and the CDF parameter information of the Pcell, based on the FMEA, optimizing the test case set to be optimized, and obtaining an optimized test case set. According to the technical scheme, the number of the test cases in the test case set can be reduced while the coverage rate of the test case set is guaranteed, the quality guarantee period can be effectively shortened, and the quality guarantee cost is reduced; and the method is simple to implement, does not depend on commercialized software, and can effectively reduce software cost.

Description

Technical Field

[0001] The present invention relates to the field of electronic design automation, and in particular to an optimization method and an optimization device and a storage medium for a test case set. Background Art

[0002] Electronic design automation (EDA) is a category of systems used to assist in the design of electronic systems and devices. Such electronic design automation systems work in a design process that allows system designers to create and analyze complex systems. Tools for device design, simulation, analysis, and verification are part of many electronic design automation systems.

[0003] A process design kit (PDK) is a complete set of process files for analog / mixed-signal IC circuit design. It is a data platform that connects IC design and IC process manufacturing. It can achieve seamless integration of IC process data / models with IC design environment / tools, shorten the design cycle, and improve design capacity and efficiency.

[0004] The parameterized cell (Pcell) in the PDK is a graphical programmable cell. Each feature of the Pcell is characterized by parameterization. The parameters that characterize the different features of the Pcell are component description format (CDF) parameters.

[0005] Quality assurance (QA) of Pcell can ensure that the Pcell layout passes the design rule check (DRC) and layout versus schematics (LVS) check.

[0006] With the advancement of technology nodes, the structure and functions of Pcell are becoming increasingly complex. The number of test cases required to ensure the quality of Pcell is becoming increasingly large. The design of test case sets is becoming more and more difficult and occupies more and more resources. Summary of the invention

[0007] The problem solved by the present invention is how to reduce the design difficulty of a test case set and reduce the resources occupied by the design.

[0008] To solve the above problems, the present invention provides a method for optimizing a test case set, comprising:

[0009] Obtain a Pcell and CDF parameter information of the Pcell, wherein the Pcell has multiple parameter modules, the parameter module has at least one CDF parameter, and the parameter information of the Pcell is suitable for characterizing the function of each of the parameter modules and the functional association between different parameter modules; obtain a test case set to be optimized; and optimize the test case set to be optimized based on FMEA according to the Pcell and the CDF parameter information of the Pcell to obtain an optimized test case set.

[0010] Optionally, the step of obtaining Pcell and CDF parameter information of the Pcell includes: obtaining Pcell, the Pcell having at least one CDF parameter; performing structural analysis on the CDF parameters of the Pcell to obtain multiple parameter modules of the Pcell; performing functional analysis on the multiple parameter modules of the Pcell to obtain parameter information of the Pcell.

[0011] Optionally, according to the CDF parameter information of Pcell and the Pcell, based on FMEA, the test case set to be optimized is optimized, and the steps of obtaining the optimized test case set include: according to the CDF parameter information of Pcell and the Pcell, in combination with the failure database, performing failure analysis on multiple Pcells to obtain the failure severity of each parameter module; performing risk analysis on the test case set to be optimized to obtain the failure occurrence rate of the parameter module and the detection rate of the test case set; establishing a SOD risk matrix according to the failure severity of the parameter module, the failure occurrence rate of the parameter module and the detection rate of the test case set; according to the SOD risk matrix, optimizing the test case set to be optimized to obtain the optimized test case set.

[0012] Optionally, in combination with the failure database, the step of performing failure analysis on the CDF parameters of the Pcell to obtain the failure severity of each parameter module includes: analyzing the consequences of the failure of each CDF parameter module of the Pcell to obtain the failure severity of each parameter module; and obtaining the failure mode and failure cause of each CDF parameter module of the Pcell based on the consequences of the failure of each CDF parameter module of the Pcell.

[0013] Optionally, the test case set to be optimized is optimized according to the SOD risk matrix, and the steps of obtaining the optimized test case set include: sorting the parameter modules according to the SOD risk matrix; obtaining the risk level of each parameter module according to the sorting sequence of the parameter modules; and optimizing the test case set to be optimized according to the risk level of the parameter module.

[0014] Optionally, in the step of obtaining the risk level of each parameter module, the risk level of each parameter module is a high risk level, a medium risk level and a low risk level, respectively; in the step of optimizing the test case set to be optimized according to the risk level of the parameter module, high-precision test cases, medium-precision test cases and low-precision test cases are designed respectively according to the parameter modules with high risk level, the parameter modules with medium risk level and the parameter modules with low risk level to optimize the test case set.

[0015] Accordingly, the present invention also provides a test case set optimization device, comprising:

[0016] A basic data module, wherein the basic data module is suitable for obtaining Pcell and CDF parameter information of the Pcell, wherein the Pcell has multiple parameter modules, wherein the parameter module has at least one CDF parameter, and wherein the parameter information of the Pcell is suitable for characterizing the function of each of the parameter modules and the functional association between different parameter modules; an acquisition module, wherein the acquisition module is suitable for obtaining a set of test cases to be optimized; and an optimization module, wherein the optimization module is suitable for optimizing the set of test cases to be optimized based on FMEA according to the Pcell and the CDF parameter information of the Pcell, to obtain an optimized set of test cases.

[0017] Optionally, the basic data module includes: a capture unit, which is suitable for obtaining Pcell, and the Pcell has at least one CDF parameter; a structural analysis unit, which is suitable for performing structural analysis on the CDF parameters of the Pcell to obtain multiple parameter modules of the Pcell; and a functional analysis unit, which is suitable for performing functional analysis on multiple parameter modules of the Pcell to obtain parameter information of the Pcell.

[0018] Optionally, the optimization module includes: a failure analysis unit, in which a failure database is pre-stored, and the failure analysis unit is suitable for performing failure analysis on multiple Pcells according to Pcell and CDF parameter information of the Pcell in combination with the failure database to obtain the failure severity of each parameter module; a risk analysis unit, which is suitable for performing risk analysis on the test case set to be optimized to obtain the failure occurrence degree of the parameter module and the detection degree of the test case set; a construction unit, which is suitable for establishing a SOD risk matrix according to the failure severity of the parameter module, the failure occurrence degree of the parameter module and the detection degree of the test case set; and an optimization unit, which is suitable for optimizing the test case set to be optimized according to the SOD risk matrix to obtain an optimized test case set.

[0019] Optionally, the failure analysis unit includes: a failure consequence analyzer, which is suitable for analyzing the consequences of failure of each CDF parameter module of the Pcell to obtain the failure severity of each parameter module; and a mode cause analyzer, which is suitable for obtaining the failure mode and failure cause of each CDF parameter module of the Pcell according to the consequences of failure of each CDF parameter module of the Pcell.

[0020] Optionally, the optimization unit includes: a sorter, which is suitable for sorting the parameter modules according to the SOD risk matrix; a classifier, which is suitable for obtaining the risk level of each parameter module according to the sorting sequence of the parameter modules; and an optimizer, which is suitable for optimizing the set of test cases to be optimized according to the risk level of the parameter modules.

[0021] Optionally, the classifier sets the risk level of each parameter module to a high risk level, a medium risk level and a low risk level, respectively; the optimizer forms high-precision test cases, medium-precision test cases and low-precision test cases according to the parameter modules with high risk levels, the parameter modules with medium risk levels and the parameter modules with low risk levels, respectively, to optimize the test case set.

[0022] In addition, the present invention also provides a storage medium on which a computer program is stored. The computer program is executed by a processor to implement the steps of the method of the present invention.

[0023] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0024] In the technical solution of the present invention, according to the Pcell and the CDF parameter information of the Pcell, based on FMEA, the test case set to be optimized is optimized to obtain the optimized test case set. The technical solution of the present invention can reduce the number of test cases in the test case set while ensuring the coverage of the test case set, can effectively shorten the quality assurance cycle, and reduce the quality assurance cost; and the method is simple to implement, does not need to rely on commercial software, and can effectively reduce software costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of an embodiment of a test case set optimization method of the present invention;

[0026] Figure 2 yes Figure 1 A schematic flow chart of the steps of obtaining Pcell and CDF parameter information of the Pcell in the test case set optimization method embodiment shown;

[0027] Figure 31 is a flowchart of the steps of optimizing the test case set to be optimized based on FMEA according to the Pcell and the CDF parameter information of the Pcell, and obtaining the optimized test case set;

[0028] Figure 4 1 is a flow chart showing the steps of optimizing the test case set to be optimized according to the SOD risk matrix to obtain the optimized test case set in the test case set optimization method embodiment;

[0029] Figure 5 It is a functional block diagram of an embodiment of an optimization device for a test case set of the present invention. DETAILED DESCRIPTION

[0030] As can be seen from the background technology, the existing methods for quality assurance of Pcell have the problems of difficulty in designing test case sets and high resource consumption.

[0031] One existing method for quality assurance of Pcell is manual quality assurance, specifically, by designing different CDF parameter combinations to construct corresponding Pcell test cases, and manually performing DRC and LVS checks on each test case through tools.

[0032] In the manual quality assurance method, in order to ensure the quality assurance effect, the test cases in the test case set need to have a considerable coverage rate; however, the high coverage rate of the test case set leads to a large number of test cases, time-consuming and labor-intensive construction, a long quality assurance cycle, and high labor costs.

[0033] Another method is to use commercial software for quality assurance. Through commercial quality assurance software, test cases with different CDF parameter combinations can be automatically generated, the number of test cases can be adjusted based on accuracy, and DRC and LVS tools can be automatically called for inspection. However, commercial software licenses are expensive, and there are still problems with the large number of test cases and the high resource consumption.

[0034] In order to solve the technical problem, the present invention provides a method for optimizing a test case set, comprising:

[0035] Obtain a Pcell and CDF parameter information of the Pcell, wherein the Pcell has multiple parameter modules, the parameter module has at least one CDF parameter, and the parameter information of the Pcell is suitable for characterizing the function of each of the parameter modules and the functional association between different parameter modules; obtain a test case set to be optimized; and optimize the test case set to be optimized based on FMEA according to the Pcell and the CDF parameter information of the Pcell to obtain an optimized test case set.

[0036] The technical solution of the present invention can reduce the number of test cases in the test case set while ensuring the coverage of the test case set, which can effectively shorten the quality assurance cycle and reduce the quality assurance cost; and the method is simple to implement and does not rely on commercial software, which can effectively reduce software costs.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0038] refer to Figure 1 , showing a flow chart of an embodiment of a test case set optimization method of the present invention.

[0039] The optimization method comprises:

[0040] Step S110, obtaining a Pcell and CDF parameter information of the Pcell, wherein the Pcell has a plurality of parameter modules, the parameter module has at least one CDF parameter, and the parameter information of the Pcell is suitable for characterizing the function of each parameter module and the functional association between different parameter modules;

[0041] Step S120, obtaining a test case set to be optimized;

[0042] Step S130 , optimizing the test case set to be optimized based on FMEA according to the Pcell and the CDF parameter information of the Pcell, to obtain an optimized test case set.

[0043] The technical solution of the test case set optimization method embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0044] like Figure 1 As shown, firstly, step S110 is performed to obtain the Pcell and the CDF parameter information of the Pcell.

[0045] A parameterized cell (Pcell) is a graphical programmable cell. A Pcell represents a part or component of a circuit. A Pcell has at least one parameter, namely a component description format (CDF) parameter.

[0046] Pcells can flexibly represent different units in a circuit design by setting different CDF parameters. Pcells with set CDF parameters can be used as part of a circuit design; Pcell instances with set CDF parameters can create structures of associated components in a larger circuit design. Different types of devices have their own Pcells. Since the properties of different types of devices, such as structure, function, and connection relationship, are different, the CDF parameters of Pcells of different types of devices are different.

[0047] like Figure 2 As shown, in some embodiments of the present invention, step S110, the step of obtaining Pcell and CDF parameter information of the Pcell includes: step S111, obtaining Pcell, the Pcell having at least one CDF parameter; step S112, performing structural analysis on the CDF parameters of the Pcell to obtain multiple parameter modules of the Pcell; step S113, performing functional analysis on the multiple parameter modules of the Pcell to obtain parameter information of the Pcell.

[0048] Step S111, the step of obtaining Pcell, that is, obtaining Pcell and all its CDF parameters. Specifically, in step S111, in the step of obtaining Pcell, the Pcell of each type of device and the CDF parameters of the Pcell of each type of device are obtained. For example, in the step of obtaining Pcell, the Pcell of the poly resistor device and its CDF parameters are obtained.

[0049] Step S112, structural analysis is performed on the CDF parameters of the Pcell, that is, the CDF parameters of the Pcell are classified to divide into parameter modules, each of which includes at least one CDF parameter, and the CDF parameters in different parameter modules are related to different properties of the device.

[0050] For example, in the step of performing structural analysis on the CDF parameters of the Pcell, structural analysis is performed on the CDF parameters of the Pcell of the poly resistor device to obtain multiple parameter modules of the Pcell of the poly resistor device, and the multiple parameter modules of the Pcell of the poly resistor device include: a shape parameter module, a connection parameter module, a tap parameter module and a resistance parameter module, etc., wherein the CDF parameters in the shape parameter module are related to the shape of the poly resistor device, the CDF parameters in the connection parameter module are related to the connection relationship of the poly resistor device, and the CDF parameters in the resistance parameter module are related to the resistance of the poly resistor device.

[0051] Step S113, performing functional analysis on the multiple parameter modules of the Pcell, that is, identifying and analyzing the functions of the multiple parameter modules of the Pcell and the associations between the modules, obtaining the function of each parameter module and the associations between different parameter modules, that is, obtaining parameter information of the Pcell.

[0052] For example, in the step of performing functional analysis on multiple parameter modules of the Pcell, functional analysis is performed on multiple parameter modules of the Pcell of the poly resistor device to obtain parameter information of the Pcell of the poly resistor device, and the parameter information of the Pcell of the poly resistor device includes: the shape parameter module is used to adjust the shape of the poly resistor device, the connection parameter module is used to adjust the connection relationship of the poly resistor device, the resistance parameter module is used to adjust the resistance of the poly resistor device, etc.; the parameter information of the Pcell of the poly resistor device also includes: the shape parameter module is associated with the resistance parameter module, the resistance parameter module is associated with the connection parameter module, etc.

[0053] The function of the connection parameter module is to adjust the connection relationship of the poly resistor device, that is, to determine the series and parallel connection mode of multiple resistors; since the resistance value will change due to the change in the series and parallel connection mode of the resistors, the connection parameter module is associated with the organization parameter module; since the shape of the poly resistor will affect the resistance value of the poly resistor, the shape parameter module is associated with the resistance parameter module.

[0054] It should be noted that in some embodiments, only Pcell and its CDF parameters can be obtained, so it is necessary to obtain the parameter information of the Pcell by performing structural analysis and functional analysis on the Pcell and its CDF parameters. In other embodiments of the present invention, the Pcell and its CDF parameter information can also be directly obtained.

[0055] Continue to refer Figure 1 The optimization method further includes: executing step S120 to obtain a test case set to be optimized.

[0056] The test case set is used to test a type of device. The test case set includes: multiple test cases, and different test cases are Pcells with different CDF parameter settings. For example, in step S120, in the step of obtaining the test case set to be optimized, the test case set to be optimized is used to test poly resistor devices, and the multiple test cases in the test case set are poly resistor devices with different CDF parameters.

[0057] After obtaining the test case set to be optimized, step S130 is executed to optimize the test case set to be optimized based on FMEA.

[0058] FMEA stands for potential failure mode and effects analysis. FMEA is an analytical method used to identify potential failure modes and their causes. FMEA can assess the potential technical risk of failure in products / processes, analyze the causes and effects of failure, record prevention and detection measures, and make recommendations for risk reduction measures.

[0059] Optimizing the test case set to be optimized based on FMEA means discovering the weaknesses of the test case set to be optimized through FMEA, determining the defects of the test case set to be optimized, and optimizing the test case to be optimized based on these results. Therefore, optimizing the test case set to be optimized based on FMEA can reduce the number of test cases in the test case set while ensuring the coverage of the test case set, effectively shorten the quality assurance cycle, and reduce the quality assurance cost; and the method is simple to implement, does not need to rely on commercial software, and can effectively reduce software costs.

[0060] like Figure 3 As shown, in some embodiments of the present invention, step S130, based on FMEA, optimizing the test case set to be optimized according to Pcell and the CDF parameter information of the Pcell, and the step of obtaining the optimized test case set includes: step S131, based on Pcell and the CDF parameter information of the Pcell, combined with the failure database, performing failure analysis on multiple Pcells to obtain the failure severity of each parameter module; step S132, performing risk analysis on the test case set to be optimized to obtain the failure occurrence degree of the parameter module and the detection degree of the test case set; step S133, establishing a SOD risk matrix according to the failure severity of the parameter module, the failure occurrence degree of the parameter module and the detection degree of the test case set; step S134, optimizing the test case set to be optimized according to the SOD risk matrix to obtain the optimized test case set.

[0061] The severity S of FMEA refers to the evaluation level of the most serious consequence of a specific failure mode. Specifically, in step S131, failure analysis is used to obtain the failure severity S of each parameter module. The failure severity S is suitable for characterizing the evaluation level of the consequence of failure of a parameter module of the Pcell. The failure severity S of different parameter modules of various types of devices Pcell can be obtained by analyzing the consequences of failure of various parameter modules of various types of devices Pcell on circuit design.

[0062] In some embodiments, step S131, the step of performing failure analysis on multiple CDF parameter modules of the Pcell to obtain the failure severity of each parameter module includes: analyzing the consequences of failure of each CDF parameter module of the Pcell to obtain the failure severity of each parameter module; and obtaining the failure mode and failure cause of each CDF parameter module of the Pcell based on the consequences of failure of each CDF parameter module of the Pcell.

[0063] It should be noted that in some exemplary embodiments, the optimization method further includes: establishing a failure database. Specifically, the failure database can be established by summarizing experience to store information such as device types and associated parameter modules involved in various circuit design problems.

[0064] The Occurrence O of FMEA, i.e., the frequency of occurrence, refers to the probability of a specific cause / mechanism occurring, where this cause / mechanism will cause the failure mode to occur within the design life. The Detection D of FMEA refers to the level of the best detection control listed in the current design control discovery column. Both Occurrence O and Detection D are relative ratings within the scope of an FMEA, and do not absolutely reflect the actual probability of occurrence.

[0065] Specifically, step S132 is to perform risk analysis on the test case set to be optimized, that is, to obtain the failure occurrence degree of the parameter module and the detection degree of the test case set. The failure occurrence degree O of the parameter module refers to the possibility of failure of different parameter modules of Pcell of each type of device when the test case set to be optimized is tested; the detection degree D of the test case set to be optimized refers to the possibility of failure of different parameter modules of Pcell of each type of device detected by the test case set to be optimized.

[0066] Continue to refer Figure 3 After obtaining the failure severity S, the failure occurrence degree O and the detection degree D, step S133 is executed to establish a SOD risk matrix.

[0067] The SOD risk matrix of FMEA includes a three-dimensional evaluation system of severity S, occurrence O and detection D, among which severity S, occurrence O and detection D are in a matrix relationship and are the evaluation basis of the FMEA method.

[0068] Specifically, the SOD risk matrix established in step S133, that is, the three-dimensional matrix established by jointly establishing the failure severity S of the parameter module, the failure occurrence degree O of the parameter module and the detection degree D of the test case set to be optimized, can be implemented in the form of a table, that is, establishing a three-dimensional table of the failure severity S of the parameter module, the failure occurrence degree O of the parameter module and the detection degree D of the test case set to be optimized.

[0069] After the SOD risk matrix is ​​established, step S134 is executed to obtain an optimized test case set.

[0070] Specifically, step S134, according to the table content of the SOD risk matrix, the test case set to be optimized is optimized to obtain an optimized test case set. Specifically, the step of optimizing the test case set to be optimized may include: deleting test cases in the test case set to be optimized, adding test cases to the test case set to be optimized, replacing test cases in the test case set to be optimized, and the like.

[0071] like Figure 4 As shown, in some embodiments, step S134, optimizing the test case set to be optimized according to the SOD risk matrix, and the step of obtaining the optimized test case set includes: step S134a, sorting the parameter modules according to the SOD risk matrix; step S134b, obtaining the risk level of each parameter module according to the sorting sequence of the parameter modules; step S134c, optimizing the test case set to be optimized according to the risk level of the parameter module.

[0072] Specifically, step S134a, according to the SOD risk matrix, the parameter modules are sorted, that is, according to the table content of the SOD risk matrix, the parameter modules are sorted based on the risk level. For example, the parameter modules are sorted from low to high according to the risk level.

[0073] In step S134b, in the step of obtaining the risk level of each parameter module, the risk level of each parameter module can be graded according to the number of grades between the lowest risk level and the highest risk level. In some exemplary embodiments, in step S134b, in the step of obtaining the risk level of each parameter module, the risk level of each parameter module is respectively a high risk level, a medium risk level and a low risk level, that is, from the lowest risk level to the highest risk level, the risk level is divided into three levels, namely, low risk, medium risk and high risk.

[0074] Step S134c, in the step of optimizing the test case set to be optimized, test cases of different precisions can be set based on parameter modules of different risk levels to optimize the test case set to be tested. In some exemplary embodiments, the risk level of each parameter module is a high risk level, a medium risk level, and a low risk level, respectively; therefore, according to the risk level of the parameter module, in the step of optimizing the test case set to be optimized, high-precision test cases, medium-precision test cases, and low-precision test cases are designed according to the parameter modules of high risk level, the parameter modules of medium risk level, and the parameter modules of low risk level, respectively, to optimize the test case set.

[0075] Table 1, SOD risk matrix

[0076]

[0077] In addition to showing the SOD risk matrix, Table 1 also shows the corresponding potential failure modes, potential failure consequences, potential failure causes / mechanisms, and the ranking results of different parameter modules. As shown in Table 1, among the multiple parameter modules of the Pcell of the poly resistor device, the shape parameter module is ranked first and has the highest risk level. In the process of optimizing the test case set to be optimized, the test case set to be optimized is optimized with high precision, such as selecting the maximum value, the minimum value, and two random values ​​between the maximum value and the minimum value to design test cases; the connection parameter module is ranked last and has the lowest risk level. In the process of optimizing the test case set to be optimized, the test case set to be optimized is optimized with low precision, such as randomly selecting one value to design test cases; the tap parameter module and the resistance parameter module are ranked between the shape parameter module and the connection parameter module. In the process of optimizing the test case set to be optimized, the test case set to be optimized is optimized with medium precision, such as randomly selecting two values ​​to design test cases.

[0078] Based on the risk level, the test cases in the test case set to be optimized are adjusted. The coverage of the test case set on parameter modules with high risk levels is improved by setting high-precision test cases; the number of overall test cases in the test case set is reduced by setting low-precision test cases; and the balance between the number of test cases and coverage in the test case set is achieved by setting medium-precision test cases.

[0079] Correspondingly, the present invention also provides a device for optimizing a test case set.

[0080] refer to Figure 5 , showing a functional block diagram of an embodiment of an optimization device for a test case set of the present invention.

[0081] The optimization device comprises:

[0082] A basic data module 210, wherein the basic data module 210 is suitable for obtaining Pcell and CDF parameter information of the Pcell, wherein the Pcell has multiple parameter modules, wherein the parameter module has at least one CDF parameter, and wherein the parameter information of the Pcell is suitable for characterizing the function of each of the parameter modules and the functional association between different parameter modules; an acquisition module 220, wherein the acquisition module 220 is suitable for obtaining a set of test cases to be optimized; and an optimization module 230, wherein the optimization module 230 is suitable for optimizing the set of test cases to be optimized based on FMEA according to the Pcell and the CDF parameter information of the Pcell, and obtaining the optimized set of test cases.

[0083] The technical solution of the test case set optimization method embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0084] The basic data module 210 obtains the Pcell and CDF parameter information of the Pcell.

[0085] A parameterized cell (Pcell) is a graphical programmable cell. A Pcell represents a part or component of a circuit. A Pcell has at least one parameter, namely a component description format (CDF) parameter.

[0086] Pcells can flexibly represent different units in a circuit design by setting different CDF parameters. Pcells with set CDF parameters can be used as part of a circuit design; Pcell instances with set CDF parameters can create structures of associated components in a larger circuit design. Different types of devices have their own Pcells. Since the properties of different types of devices, such as structure, function, and connection relationship, are different, the CDF parameters of Pcells of different types of devices are different.

[0087] like Figure 2 As shown, in some embodiments of the present invention, the basic data module 210 includes: a capture unit 211, the capture unit 211 is suitable for obtaining Pcell, and the Pcell has at least one CDF parameter; a structural analysis unit 212, the structural analysis unit 212 is suitable for performing structural analysis on the CDF parameters of the Pcell to obtain multiple parameter modules of the Pcell; a functional analysis unit 213, the functional analysis unit 213 is suitable for performing functional analysis on multiple parameter modules of the Pcell to obtain parameter information of the Pcell.

[0088] The capture unit 211 can obtain the Pcell and all its CDF parameters. Specifically, the capture unit 211 can obtain the Pcell of various types of devices and the CDF parameters of the Pcell of each type of device. For example, the capture unit 211 can obtain the Pcell of a poly resistor device and its CDF parameters.

[0089] The structure analysis unit 212 may classify the CDF parameters of the Pcell to divide them into parameter modules, each of which includes at least one CDF parameter, and the CDF parameters in different parameter modules are related to different properties of the device.

[0090] For example, the structural analysis unit 212 can perform structural analysis on the CDF parameters of the Pcell of the poly resistor device to obtain multiple parameter modules of the Pcell of the poly resistor device. The multiple parameter modules of the Pcell of the poly resistor device include: a shape parameter module, a connection parameter module, a tap parameter module and a resistance parameter module, etc., wherein the CDF parameters in the shape parameter module are related to the shape of the poly resistor device, the CDF parameters in the connection parameter module are related to the connection relationship of the poly resistor device, and the CDF parameters in the resistance parameter module are related to the resistance of the poly resistor device.

[0091] The function analysis unit 213 may identify and analyze the functions of the multiple parameter modules of the Pcell and the associations between the modules, and obtain the function of each parameter module and the associations between different parameter modules, that is, obtain the parameter information of the Pcell.

[0092] For example, the functional analysis unit 213 can perform functional analysis on multiple parameter modules of the Pcell of the poly resistor device to obtain parameter information of the Pcell of the poly resistor device, and the parameter information of the Pcell of the poly resistor device includes: the shape parameter module is used to adjust the shape of the poly resistor device, the connection parameter module is used to adjust the connection relationship of the poly resistor device, the resistance parameter module is used to adjust the resistance of the poly resistor device, etc.; the parameter information of the Pcell of the poly resistor device also includes: the shape parameter module is associated with the resistance parameter module, the resistance parameter module is associated with the connection parameter module, etc.

[0093] The function of the connection parameter module is to adjust the connection relationship of the poly resistor device, that is, to determine the series and parallel connection mode of multiple resistors; since the resistance value will change due to the change in the series and parallel connection mode of the resistors, the connection parameter module is associated with the organization parameter module; since the shape of the poly resistor will affect the resistance value of the poly resistor, the shape parameter module is associated with the resistance parameter module.

[0094] It should be noted that, in some embodiments, the basic data module 210 can only obtain Pcell and its CDF parameters, so it is necessary to obtain the parameter information of the Pcell by performing structural analysis and functional analysis on the Pcell and its CDF parameters. In other embodiments of the present invention, the basic data module can also directly obtain the Pcell and the CDF parameter information of the Pcell.

[0095] Continue to refer Figure 5 , the optimization device also includes: an acquisition module 220 that can obtain a test case set to be optimized.

[0096] The test case set is used to test a type of device. The test case set includes: multiple test cases, and different test cases are Pcells with different CDF parameter settings. For example, the test case set to be optimized obtained by the acquisition module 220 is used to test poly resistor devices, and the multiple test cases in the test case set are poly resistor devices with different CDF parameters.

[0097] The optimization device also includes an optimization module 230 that can optimize the test case set to be optimized based on FMEA.

[0098] FMEA stands for potential failure mode and effects analysis. FMEA is an analytical method used to identify potential failure modes and their causes. FMEA can assess the potential technical risk of failure in products / processes, analyze the causes and effects of failure, record prevention and detection measures, and make recommendations for risk reduction measures.

[0099] The optimization module 230 can optimize the test case set to be optimized based on FMEA, that is, the weaknesses of the test case set to be optimized can be discovered through FMEA, the defects of the test case set to be optimized can be determined, and based on these results, the test cases to be optimized can be optimized. Therefore, the optimization module 230 can optimize the test case set to be optimized based on FMEA, which can reduce the number of test cases in the test case set while ensuring the coverage of the test case set, effectively shorten the quality assurance cycle, and reduce the quality assurance cost; and the method is simple to implement, does not need to rely on commercial software, and can effectively reduce software costs.

[0100] like Figure 3 As shown, in some embodiments of the present invention, the optimization module 230 includes: a failure analysis unit 231, in which a failure database is pre-stored, and the failure analysis unit 231 is suitable for performing failure analysis on multiple Pcells according to the Pcell and the CDF parameter information of the Pcell, combined with the failure database, to obtain the failure severity of each parameter module; a risk analysis unit 232, wherein the risk analysis unit 232 is suitable for performing risk analysis on the test case set to be optimized, and obtaining the failure occurrence degree of the parameter module and the detection degree of the test case set; a construction unit 233, wherein the construction unit 233 is suitable for establishing a SOD risk matrix according to the failure severity of the parameter module, the failure occurrence degree of the parameter module and the detection degree of the test case set; an optimization unit 234, wherein the optimization unit 234 is suitable for optimizing the test case set to be optimized according to the SOD risk matrix to obtain the optimized test case set.

[0101] The severity S of FMEA refers to the evaluation level of the most serious consequence of a specific failure mode. Specifically, the failure analysis unit 231 can obtain the failure severity S of each parameter module. The failure severity S is suitable for characterizing the evaluation level of the consequence of the failure of a parameter module of the Pcell. The failure severity S of different parameter modules of various types of devices Pcell can be obtained by analyzing the consequences of the failure of various parameter modules of various types of devices Pcell on the circuit design.

[0102] In some embodiments, the failure analysis unit 231 includes: a failure consequence analyzer 231a, which is suitable for analyzing the consequences of failure of each CDF parameter module of the Pcell to obtain the failure severity of each parameter module; a mode cause analyzer 231b, which is suitable for obtaining the failure mode and failure cause of each CDF parameter module of the Pcell based on the consequences of failure of each CDF parameter module of the Pcell.

[0103] The Occurrence O of FMEA, i.e., the frequency of occurrence, refers to the probability of a specific cause / mechanism occurring, where this cause / mechanism will cause the failure mode to occur within the design life. The Detection D of FMEA refers to the level of the best detection control listed in the current design control discovery column. Both Occurrence O and Detection D are relative ratings within the scope of an FMEA, and do not absolutely reflect the actual probability of occurrence.

[0104] Specifically, the risk analysis unit 232 may obtain the failure occurrence degree O of the parameter module and the detection degree D of the test case set. The failure occurrence degree O of the parameter module refers to the possibility of failure of different parameter modules of Pcell of each type of device when the test case set to be optimized is tested; the detection degree D of the test case set to be optimized refers to the possibility of failure of different parameter modules of Pcell of each type of device detected by the test case set to be optimized.

[0105] Continue to refer Figure 3 After the failure analysis unit 231 and the risk analysis unit 232 obtain the failure severity S, the failure occurrence degree O and the detection degree D respectively, the construction unit 233 can establish a SOD risk matrix.

[0106] The SOD risk matrix of FMEA includes a three-dimensional evaluation system of severity S, occurrence O and detection D, among which severity S, occurrence O and detection D are in a matrix relationship and are the evaluation basis of the FMEA method.

[0107] Specifically, the SOD risk matrix established by the construction unit 233, that is, the three-dimensional matrix established by combining the failure severity S of the parameter module, the failure occurrence degree O of the parameter module and the detection degree D of the test case set to be optimized, can be implemented in the form of a table, that is, establishing a three-dimensional table of the failure severity S of the parameter module, the failure occurrence degree O of the parameter module and the detection degree D of the test case set to be optimized.

[0108] After establishing the SOD risk matrix, the optimization unit 234 can use it to obtain an optimized test case set.

[0109] Specifically, the optimization unit 234 can optimize the test case set to be optimized according to the table content of the SOD risk matrix, so as to obtain an optimized test case set. Specifically, the optimization unit 234 can delete test cases in the test case set to be optimized, add test cases to the test case set to be optimized, replace test cases in the test case set to be optimized, etc.

[0110] like Figure 4As shown, in some embodiments, the optimization unit 234 includes: a sorter 234a, wherein the sorter 234a is suitable for sorting the parameter modules according to the SOD risk matrix; a classifier 234b, wherein the classifier 234b is suitable for obtaining the risk level of each parameter module according to the sorting sequence of the parameter modules; and an optimizer 234c, wherein the optimizer 234c is suitable for optimizing the set of test cases to be optimized according to the risk level of the parameter modules.

[0111] Specifically, the sorter 234a can sort the parameter modules, that is, sort the parameter modules based on the risk level according to the table content of the SOD risk matrix. For example, the parameter modules are sorted from low to high according to the risk level.

[0112] The classifier 234b may classify the risk level of each parameter module according to the number of levels between the lowest risk level and the highest risk level. In some exemplary embodiments, the classifier 234b may classify the risk level of each parameter module as a high risk level, a medium risk level, and a low risk level, that is, from the lowest risk level to the highest risk level, the risk level is divided into three levels, namely, low risk, medium risk, and high risk.

[0113] The optimizer 234c can set test cases of different precisions based on parameter modules of different risk levels to optimize the test case set to be tested. In some exemplary embodiments, the risk levels of each parameter module are high risk level, medium risk level and low risk level respectively; therefore, the optimizer 234c forms high-precision test cases, medium-precision test cases and low-precision test cases according to the parameter modules of high risk level, medium risk level and low risk level respectively to optimize the test case set.

[0114] In addition, the present invention also provides a readable storage medium on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the test case set optimization method of the present invention.

[0115] The computer program stored on the readable storage medium is executed by the processor to implement the steps of the test case set optimization method of the present invention. The specific technical solution of the readable storage medium can refer to the embodiment of the test case set optimization method mentioned above, and the present invention will not repeat it here.

[0116] In summary, according to the Pcell and the CDF parameter information of the Pcell, based on FMEA, the test case set to be optimized is optimized to obtain the optimized test case set. The technical solution of the present invention can reduce the number of test cases in the test case set while ensuring the coverage of the test case set, which can effectively shorten the quality assurance cycle and reduce the quality assurance cost; and the method is simple to implement, does not need to rely on commercial software, and can effectively reduce software costs.

[0117] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for optimizing a test case set, characterized in that: include: Obtaining a Pcell and CDF parameter information of the Pcell, wherein the Pcell has a plurality of parameter modules, the parameter module has at least one CDF parameter, and the parameter information of the Pcell is suitable for characterizing the function of each of the parameter modules and the functional association between different parameter modules; Obtain the test case set to be optimized; According to the Pcell and the CDF parameter information of the Pcell, based on FMEA, the test case set to be optimized is optimized to obtain an optimized test case set.

2. The optimization method according to claim 1, characterized in that: The step of obtaining the Pcell and the CDF parameter information of the Pcell includes: Obtaining a Pcell, wherein the Pcell has at least one CDF parameter; Performing structural analysis on the CDF parameters of the Pcell to obtain a plurality of parameter modules of the Pell; Functional analysis is performed on multiple parameter modules of the Pcell to obtain parameter information of the Pcell.

3. The optimization method according to claim 1, characterized in that: According to the Pcell and the CDF parameter information of the Pcell, based on FMEA, the test case set to be optimized is optimized, and the steps of obtaining the optimized test case set include: According to the Pcell and the CDF parameter information of the Pcell, combined with the failure database, a failure analysis is performed on a plurality of the Pcells to obtain the failure severity of each parameter module; Performing risk analysis on the test case set to be optimized to obtain the failure occurrence degree of the parameter module and the detection degree of the test case set; Establishing a SOD risk matrix according to the failure severity of the parameter module, the failure occurrence degree of the parameter module and the detection degree of the test case set; According to the SOD risk matrix, the test case set to be optimized is optimized to obtain an optimized test case set.

4. The optimization method according to claim 3, characterized in that: In combination with the failure database, the step of performing failure analysis on the CDF parameters of the Pcell to obtain the failure severity of each parameter module includes: Analyze the consequences of failure of each CDF parameter module of the Pcell to obtain the failure severity of each parameter module; According to the consequence of the failure of each CDF parameter module of the Pcell, the failure mode and failure cause of each CDF parameter module of the Pcell are obtained.

5. The optimization method according to claim 3, characterized in that: According to the SOD risk matrix, the test case set to be optimized is optimized, and the step of obtaining the optimized test case set includes: sorting the parameter modules according to the SOD risk matrix; According to the sorting sequence of the parameter modules, the risk level of each parameter module is obtained; The test case set to be optimized is optimized according to the risk level of the parameter module.

6. The optimization method according to claim 5, characterized in that: In the step of obtaining the risk level of each parameter module, the risk level of each parameter module is respectively a high risk level, a medium risk level and a low risk level; In the step of optimizing the test case set to be optimized according to the risk level of the parameter module, high-precision test cases, medium-precision test cases and low-precision test cases are designed respectively according to the parameter modules with high risk level, the parameter modules with medium risk level and the parameter modules with low risk level to optimize the test case set.

7. A test case set optimization device, characterized in that: A basic data module, wherein the basic data module is suitable for obtaining a Pcell and CDF parameter information of the Pcell, wherein the Pcell has a plurality of parameter modules, wherein the parameter module has at least one CDF parameter, and wherein the parameter information of the Pcell is suitable for characterizing a function of each of the parameter modules and a functional association between different parameter modules; An acquisition module, wherein the acquisition module is suitable for obtaining a set of test cases to be optimized; The optimization module is suitable for optimizing the test case set to be optimized based on the Pcell and the CDF parameter information of the Pcell and based on FMEA to obtain the optimized test case set.

8. The optimization device according to claim 7, characterized in that: The basic data module includes: A capture unit, the capture unit being adapted to obtain a Pcell, the Pcell having at least one CDF parameter; A structural analysis unit, wherein the structural analysis unit is adapted to perform structural analysis on the CDF parameters of the Pcell to obtain a plurality of parameter modules of the Pell; A function analysis unit, wherein the function analysis unit is suitable for performing function analysis on a plurality of parameter modules of the Pcell to obtain parameter information of the Pcell.

9. The optimization device according to claim 7, characterized in that: The optimization module includes: A failure analysis unit, wherein a failure database is pre-stored in the failure analysis unit, and the failure analysis unit is adapted to perform failure analysis on a plurality of the Pcells according to the Pcell and the CDF parameter information of the Pcell in combination with the failure database to obtain the failure severity of each parameter module; A risk analysis unit, wherein the risk analysis unit is adapted to perform risk analysis on the test case set to be optimized, and obtain the failure occurrence degree of the parameter module and the detection degree of the test case set; A construction unit, wherein the construction unit is adapted to establish a SOD risk matrix according to the failure severity of the parameter module, the failure occurrence degree of the parameter module and the detection degree of the test case set; An optimization unit, wherein the optimization unit is adapted to optimize the test case set to be optimized according to the SOD risk matrix to obtain an optimized test case set.

10. The optimization device according to claim 9, characterized in that: The failure analysis unit comprises: A failure consequence analyzer, wherein the failure consequence analyzer is adapted to analyze the consequence of failure of each CDF parameter module of the Pcell to obtain the failure severity of each parameter module; A mode cause analyzer is adapted to obtain the failure mode and failure cause of each CDF parameter module of the Pcell according to the consequence of the failure of each CDF parameter module of the Pcell.

11. The optimization device according to claim 9, characterized in that: The optimization unit comprises: a sorter adapted to sort the parameter modules according to the SOD risk matrix; a classifier, the classifier being adapted to obtain a risk level of each parameter module according to the sorting sequence of the parameter modules; An optimizer, wherein the optimizer is adapted to optimize the set of test cases to be optimized according to the risk level of the parameter module.

12. The optimization device according to claim 11, characterized in that: The classifier sets the risk level of each parameter module to a high risk level, a medium risk level, and a low risk level respectively; The optimizer forms high-precision test cases, medium-precision test cases and low-precision test cases according to the parameter modules of high-risk level, medium-risk level and low-risk level respectively to optimize the test case set.

13. A storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 6.

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