Method, device, computer equipment and storage medium for determining influence coefficient

Abstract

The present disclosure relates to a method, device, computer equipment and storage medium for determining an influence coefficient. The method for determining an influence coefficient includes: obtaining a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, and determining a third parameter value of a third preset parameter based on the first parameter value and the second parameter value; in response to the first parameter value, the second parameter value and the third parameter value being suitable for fitting a first linear regression model, determining a target fitting result based on the fitting result of the first linear regression model for the first parameter value, the second parameter value and the third parameter value; and determining the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter based on the target fitting result. This method can intuitively see the degree of influence of the random deviation introduced by the process manufacturing and the process angle deviation between different wafers on the integrated circuit chip of the same components on the same wafer area, so as to ensure that the designed circuit can still meet the performance requirements after tape-out.

Description

Technical Field

[0001] The present disclosure relates to the technical field of integrated circuit design, and in particular to a method, apparatus, computer equipment, and storage medium for determining an influence coefficient. Background Art

[0002] During the design of integrated circuits, circuits need to be simulated to achieve the expected results. During the simulation process, the main purpose is to simulate the impact of random deviations introduced by process manufacturing on the same components on the same wafer area and process angle deviations between different wafers on the integrated circuit. This allows for consideration of deviations that may be introduced by process manufacturing during the circuit design process to ensure that the designed circuit can still meet performance requirements when it is tape-out. Therefore, being able to intuitively view the impact of random deviations and process angle deviations on the integrated circuit is a necessary measure to facilitate designers in debugging and optimizing circuits. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a method, apparatus, computer device and storage medium for determining an influence coefficient.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for determining an influence coefficient is provided, the method comprising:

[0005] Obtaining a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same components on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after the chip simulation;

[0006] Determining a third parameter value of a third preset parameter according to the first parameter value and the second parameter value; the third preset parameter is related to the first preset parameter and the second preset parameter;

[0007] In response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting of a first linear regression model, determining a target fitting result based on a fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value;

[0008] According to the target fitting result, influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are determined.

[0009] In an exemplary embodiment, determining a target fitting result based on a fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value includes:

[0010] When a parameter value of a preset parameter of the first linear regression model is greater than or equal to a first preset threshold, determining the fitting result of the first linear regression model as the target fitting result;

[0011] When the parameter value of the preset parameter of the first linear regression model is less than the first preset threshold, the first parameter value, the second parameter value, and the third parameter value are refitted using a second linear regression model to obtain a fitting result of the second linear regression model, and the fitting result of the second linear regression model is determined as the target fitting result; the fitting ability of the second linear regression model is greater than the fitting ability of the first linear regression model.

[0012] In an exemplary embodiment, determining, based on the target fitting result, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter includes:

[0013] Determining a ratio of a coefficient of the first parameter value to a sum of all coefficients in the target fitting result as an influence coefficient of the first preset parameter on the third preset parameter;

[0014] The ratio of the coefficient of the second parameter value in the target fitting result to the sum of all coefficients is determined as the influence coefficient of the second preset parameter on the third preset parameter.

[0015] In an exemplary embodiment, the first preset parameter includes a plurality of first sub-parameters, the second preset parameter includes a plurality of second sub-parameters, and the method further includes:

[0016] displaying the influence coefficient of each first sub-parameter on the third preset parameter, and the influence coefficient of each second sub-parameter on the third preset parameter; and / or,

[0017] The sum of the influence coefficients of all the first sub-parameters on the third preset parameter and the sum of the influence coefficients of all the second sub-parameters on the third preset parameter are displayed.

[0018] In an exemplary embodiment, the method further comprises:

[0019] A preset identifier is displayed, where the preset identifier represents an identifier of a linear regression model used when fitting the first parameter value, the second parameter value, and the third parameter value.

[0020] In an exemplary embodiment, the method further comprises:

[0021] Using the first parameter value and the second parameter value as independent variables and the third parameter value as a dependent variable, fitting the first parameter value, the second parameter value, and the third parameter value using the first linear regression model to determine a fitting result;

[0022] If the test parameter of the fitting result is greater than or equal to a second preset threshold, determining that the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model;

[0023] If the test parameter of the fitting result is less than the second preset threshold, it is determined that the first parameter value, the second parameter value, and the third parameter value are not suitable for fitting the first linear regression model.

[0024] In an exemplary embodiment, the method further comprises:

[0025] In response to the first parameter value, the second parameter value, and the third parameter value being unsuitable for fitting the first linear regression model, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are marked in a preset manner.

[0026] According to a second aspect of an embodiment of the present disclosure, a device for determining an influence coefficient is provided, the device comprising:

[0027] A first acquisition module is configured to acquire a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same components on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after the chip simulation;

[0028] a second acquisition module configured to determine a third parameter value of a third preset parameter according to the first parameter value and the second parameter value; the third preset parameter is related to the first preset parameter and the second preset parameter;

[0029] a fitting module configured to, in response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting a first linear regression model, determine a target fitting result based on a fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value;

[0030] A determination module is configured to determine, based on the target fitting result, an influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter.

[0031] According to a third aspect of an embodiment of the present disclosure, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when executing the computer program.

[0032] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0033] The above-mentioned method disclosed in the present invention has the following beneficial effects: it can intuitively see the degree of influence of random deviations introduced by process manufacturing on the same components in the same wafer area and process angle deviations between different wafers on the integrated circuit chip, which makes it easier to take into account the deviations that may be introduced by process manufacturing during the circuit design process, and ensure that the designed circuit can still meet the performance requirements after it is taped out.

[0034] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0036] Figure 1 is a flow chart showing a method for determining an influence coefficient according to an exemplary embodiment;

[0037] Figure 2 is a parameter diagram illustrating a first preset parameter according to an exemplary embodiment;

[0038] Figure 3 is a schematic diagram illustrating a first parameter value of a first preset parameter according to an exemplary embodiment;

[0039] Figure 4 is a parameter diagram illustrating a second preset parameter according to an exemplary embodiment;

[0040] Figure 5 is a schematic diagram illustrating a second parameter value of a second preset parameter according to an exemplary embodiment;

[0041] Figure 6 is a block diagram of a device for determining an influence coefficient according to an exemplary embodiment;

[0042] Figure 7 The figure is a block diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0044] In an exemplary embodiment of the present disclosure, a method for determining an influence coefficient is provided, comprising: obtaining a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same component on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after chip simulation; determining a third parameter value of a third preset parameter based on the first parameter value and the second parameter value; the third preset parameter is related to the first preset parameter and the second preset parameter; in response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting a first linear regression model, determining a target fitting result based on the fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value; and determining the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter based on the target fitting result. This method can intuitively see the degree of influence of the random deviation of the same component on the same wafer area due to process manufacturing and the process angle deviation between different wafers on the integrated circuit chip, making it easier to consider the deviation that may be introduced by process manufacturing during the circuit design process, ensuring that the designed circuit can still meet performance requirements after tape-out.

[0045] In an exemplary embodiment of the present disclosure, a method for determining an influence coefficient is provided. Figure 1 is a flow chart showing a method for determining an influence coefficient according to an exemplary embodiment. Figure 1 As shown, the following steps are included:

[0046] Step S101, obtaining a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same components on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after chip simulation;

[0047] Step S102: determining a third parameter value of a third preset parameter according to the first parameter value and the second parameter value; the third preset parameter is related to the first preset parameter and the second preset parameter;

[0048] Step S103, in response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting of the first linear regression model, determining a target fitting result according to the fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value;

[0049] Step S104: determining the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter according to the target fitting result.

[0050] The method in the embodiments of the present disclosure is applied to computer devices, including smart phones, tablets, laptop computers, desktop computers and other electronic devices capable of executing the above steps.

[0051] In step S101, the integrated circuit chip is simulated using circuit simulation software. When simulating, any simulation method is used, such as the Monte Carlo simulation method. In this embodiment, there is no restriction on the circuit simulation software and the simulation method. The first preset parameter characterizes the random deviation parameter between the same components on the same wafer area. The random deviation parameter is caused by changes in the external conditions or production conditions of the component process, such as process changes, voltage changes, or temperature changes. The second preset parameter characterizes the process angle deviation between different wafers, which is caused by changes in conditions such as the doping concentration, diffusion depth, and etching degree of the wafer process. The first preset parameter and the second preset parameter respectively include multiple sub-parameters, and the first parameter value of each sub-parameter in the first preset parameter and the second parameter value of each sub-parameter in the second preset parameter are respectively obtained.

[0052] In one example, when simulating an integrated circuit chip using a Monte Carlo simulation method, the parameter files in the Monte Carlo simulation result directory are searched, and the parameter files include a parameter file of a first preset parameter and a parameter file of a second preset parameter:

[0053] The parameter file of the first preset parameter includes the parameter and the first parameter value. Figure 2 is a parameter diagram showing a first preset parameter according to an exemplary embodiment. Figure 2 As shown, the parameters included in the first preset parameter are displayed in a file named "compactmismatchParam", where I7.NM1.n18_dnw_ckt and I7.PM0 represent component names, and rnd_mis_2, rnd_mis_0, rnd_mis_1, and rnd_mis_fn respectively represent parameter names of parameters shared by components I7.NM1.n18_dnw_ckt and I7.PM0; Figure 3 is a schematic diagram showing a first parameter value of a first preset parameter according to an exemplary embodiment. Figure 3As shown, the first parameter value is displayed in the file named "compactmismatchData", which includes 5 sets of data, each set of data is generated under different simulation conditions, and each set of data is Figure 2 Corresponding to the parameters in, taking the first set of data as an example, the parameter value of the parameter rnd_mis_2 of the component I7.NM1.n18_dnw_ckt is 1.5594864272, the parameter value of the parameter rnd_mis_2 of the component I7.PM0 is 1.1934497802, the parameter value of the parameter rnd_mis_0 of the component I7.NM1.n18_dnw_ckt is -1.527490009, the parameter value of the parameter rnd_mis_0 of the component I7.PM0 is -0.16051347471, and so on. Figure 3 Each set of data in Figure 2 The parameter values ​​of the 8 parameters in .

[0054] The parameter file of the second preset parameter includes the parameter and the second parameter value. Figure 4 is a parameter diagram showing the second preset parameter according to an exemplary embodiment. Figure 4 As shown, the parameters included in the second preset parameter are displayed in a file named "processParam", where each line represents a parameter, such as sigma_var_mc1_08, rnd_0, etc.; Figure 5 is a schematic diagram showing a second parameter value of a second preset parameter according to an exemplary embodiment. Figure 5 As shown, the second parameter value is displayed in the file named "processData", which includes 5 sets of data, each set of data is generated under different simulation conditions, and each set of data is Figure 4 The parameters in each row correspond one to one.

[0055] In step S102, since the first and second preset parameters represent the random deviation parameters and process angle deviation parameters introduced by the manufacturing process, respectively, the first and second preset parameters will affect the simulation results of the integrated circuit chip. The third preset parameter represents the calculation result of a preset expression in the integrated circuit. The preset expression is used to calculate the voltage, current, etc. of a branch in the integrated circuit. Therefore, the first and second preset parameters will affect the third preset parameter. The third parameter value of the third preset parameter is obtained by simulation when the first preset parameter is the first parameter value and the second preset parameter is the second parameter value.

[0056] In step S103, the first linear regression model includes a multivariate linear regression model, and the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model, indicating that the distribution relationship between the first parameter value, the second parameter value, and the third parameter value conforms to the distribution of the first linear regression model. The first linear regression model is used to fit the first parameter value of the first preset parameter, the parameter value of the second preset parameter, and the parameter value of the third preset parameter to obtain a fitting result of the first linear regression model. When determining that the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model, the fitting result of the first linear regression model is used as the initial fitting result, and the target fitting result is determined based on the initial fitting result. In this embodiment, since it is necessary to determine the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter, when fitting the first parameter value, the second parameter value, and the third parameter value, the third parameter value of the third preset parameter is used as the dependent variable, and the first parameter value of the first preset parameter and the second parameter value of the second preset parameter are used as the independent variable to perform fitting.

[0057] In step S104, based on the coefficient of the first parameter value in the target fitting result, the degree of influence of the first preset parameter on the third preset parameter can be determined, and based on the coefficient of the second parameter value in the target fitting result, the degree of influence of the second preset parameter on the third preset parameter can be determined. According to the degree of influence, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter can be quantified, and the larger the influence coefficient, the greater the influence.

[0058] In an exemplary embodiment of the present disclosure, when the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model, a target fitting result is determined based on the fitting results of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value. Based on the target fitting result, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are calculated. This allows for intuitively seeing the extent to which random deviations introduced by process manufacturing for the same components on the same wafer area and process angle deviations between different wafers affect the integrated circuit chip, making it easier to consider deviations that may be introduced by process manufacturing during circuit design, ensuring that the designed circuit can still meet performance requirements after tape-out.

[0059] In an exemplary embodiment, in step S103 of the above embodiment, determining the target fitting result based on the fitting results of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value includes one of the following two situations:

[0060] First, when the parameter value of the preset parameter of the first linear regression model is greater than or equal to a first preset threshold, the fitting result of the first linear regression model is determined as the target fitting result.

[0061] The preset parameters represent the strength of the relationship between the first linear regression model and the first parameter value, the second parameter value, and the third parameter value. The larger the value of the preset parameter, the closer the relationship between the first linear regression model and the first parameter value, the second parameter value, and the third parameter value. That is, the more the distribution relationship of the first parameter value, the second parameter value, and the third parameter value conforms to the first linear regression model, the better the fitting effect. The preset parameters include the goodness of fit, i.e., R 2 The first preset threshold is an empirical value. For example, when the preset parameter is goodness of fit, the first preset threshold is 0.9. When the goodness of fit of the first linear regression model is greater than or equal to 0.9, it indicates that the fitting effect of the first linear regression model is good. At this time, the fitting result of the first linear regression model is determined as the target fitting result.

[0062] The second method is to use a second linear regression model to refit the first parameter value, the second parameter value, and the third parameter value when the parameter value of the preset parameter of the first linear regression model is less than the first preset threshold value to obtain a fitting result of the second linear regression model, and determine the fitting result of the second linear regression model as the target fitting result; the fitting ability of the second linear regression model is greater than the fitting ability of the first linear regression model.

[0063] The default parameter is the goodness of fit, that is, R 2 When the first preset threshold is 0.9, when the goodness of fit of the first linear regression model is less than 0.9, it indicates that the fitting effect of the first linear regression model is poor. In this case, the first parameter value, the second parameter value, and the third parameter value are refitted using the second linear regression model to obtain the fitting result of the second linear regression model, and the fitting result of the second linear regression model is determined as the target fitting result. The fitting ability of the second linear regression model is greater than the fitting ability of the first linear regression model. For example, when the first linear regression model is a multivariate linear regression model, the second linear regression model is a multivariate quadratic linear regression model; when the first linear regression model is a multivariate quadratic linear regression model, the second linear regression model is a multivariate cubic linear regression model.

[0064] In an exemplary embodiment, in step S104 of the above embodiment, determining the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter according to the target fitting result includes:

[0065] Determine the ratio of the coefficient of the first parameter value in the target fitting result to the sum of all coefficients as the influence coefficient of the first preset parameter on the third preset parameter;

[0066] The ratio of the coefficient of the second parameter value in the target fitting result to the sum of all coefficients is determined as the influence coefficient of the second preset parameter on the third preset parameter.

[0067] The first parameter value of the first preset parameter is recorded as a, the second parameter value of the second preset parameter is recorded as b, and the third parameter value of the third parameter is recorded as c. The fitting result of the multivariate linear regression model is used as the target fitting result, and the fitting result of the multivariate linear regression model is recorded as c=xa+yb+k, where x represents the coefficient of the first parameter value, y represents the coefficient of the second parameter value, and k represents a constant. The influence coefficient of the first preset parameter on the third preset parameter is the ratio of x to x+y, and the influence coefficient of the second preset parameter on the third preset parameter is the ratio of y to x+y. When the first preset parameter includes multiple parameters and the second preset parameter also includes multiple parameters, each parameter value in the fitting result has a corresponding coefficient, and the influence coefficient of the corresponding parameter of each parameter value on the third preset parameter can be calculated.

[0068] In some implementations, when the first preset parameter includes multiple first sub-parameters and the second preset parameter includes multiple second sub-parameters, the above embodiment further includes at least one of the following two situations after step S104:

[0069] The first type displays the influence coefficient of each first sub-parameter on the third preset parameter, and the influence coefficient of each second sub-parameter on the third preset parameter.

[0070] In one example, Table 1 shows the influence coefficient of each first sub-parameter on the third preset parameter, and the influence coefficient of each second sub-parameter on the third preset parameter. As shown in Table 1, risetime1 represents the third preset parameter, R 2 represents the goodness of fit of the first linear regression model, rnd_io_0 to rnd_io_4 represent the second sub-parameters in the second preset parameters, I7 / NM1 / n18_dnw_ckt:rnd_mis_0 and I7 / PM0:rnd_mis_0 represent the first sub-parameters in the first preset parameters, and the influence coefficient of the second sub-parameter rnd_io_0 on the third preset parameter is 6%. Similarly, the influence coefficient of each first sub-parameter on the third preset parameter and the influence coefficient of each second sub-parameter on the third preset parameter can be obtained from Table 1.

[0071] Table 1

[0072]

[0073] The second type displays the sum of the influence coefficients of all first sub-parameters on the third preset parameter, and the sum of the influence coefficients of all second sub-parameters on the third preset parameter.

[0074] In one example, Table 2 shows the sum of the influence coefficients of all first sub-parameters on the third preset parameter, and the sum of the influence coefficients of all second sub-parameters on the third preset parameter. Table 2 is calculated from Table 1. As shown in Table 2, risetime1 represents the third preset parameter, R 2 represents the goodness of fit of the first linear regression model, process represents all second sub-parameters in the second preset parameters, I7 / NM1 / n18_dnw_ckt and I7 / PM0 represent all first sub-parameters of each component in the first preset parameters, then the sum of the influence coefficients of all second sub-parameters on the third preset parameter is 98%, the sum of the influence coefficients of all first sub-parameters of component I7 / NM1 / n18_dnw_ckt on the third preset parameter is 1%, and the sum of the influence coefficients of all first sub-parameters of component I7 / PM0 on the third preset parameter is 1%.

[0075] Table 2

[0076]

[0077] In some embodiments, when the above-mentioned influence coefficient is displayed, a preset identifier is also displayed, and the preset identifier represents the identifier of the linear regression model used when fitting the first parameter value, the second parameter value, and the third parameter value.

[0078] Table 3

[0079]

[0080] In one example, when a multivariate linear regression model is used to fit the first parameter value, the second parameter value, and the third parameter value, the preset identifier is empty; when a multivariate quadratic linear regression model is used to fit the first parameter value, the second parameter value, and the third parameter value, the preset identifier is Quadratic.

[0081] For example, Table 3 shows the influence coefficient of each first sub-parameter on the third preset parameter, and the influence coefficient of each second sub-parameter on the third preset parameter. As shown in Table 3, delay1Nominal represents the third preset parameter under normal conditions, delay1C1 represents the third preset parameter under C1 conditions, risetime1Nominal represents the third preset parameter under normal conditions, risetime1C1 represents the third preset parameter under C1 conditions, delay1 and risetime1 represent different third preset parameters, R 2It represents the goodness of fit of the second linear regression model. The first 10 parameters in the first column represent the second sub-parameters in the second preset parameters, and the last 5 parameters represent the first sub-parameters in the first preset parameters. Table 3 can be used to know the influence coefficient of each first sub-parameter on each third preset parameter, the influence coefficient of each second sub-parameter on each third preset parameter, and the maximum influence coefficient of each first sub-parameter and each second sub-parameter on all third preset parameters.

[0082] Table 4 shows the sum of the influence coefficients of all first sub-parameters on each third preset parameter, and the sum of the influence coefficients of all second sub-parameters on each third preset parameter. Table 4 is calculated based on Table 3. As shown in Table 4, delay1Nominal represents the third preset parameter under normal conditions, delay1C1 represents the third preset parameter under C1 conditions, risetime1Nominal represents the third preset parameter under normal conditions, risetime1C1 represents the third preset parameter under C1 conditions, delay1 and risetime1 represent different third preset parameters, R 2 represents the goodness of fit of the second linear regression model, process represents all second sub-parameters in the second preset parameters, I7 / NM1 / n18_dnw_ckt and I7 / PM0 represent all first sub-parameters of each component in the first preset parameters, then the sum of the influence coefficients of all second sub-parameters on delay1Nominal is 100%, the sum of the influence coefficients of all first sub-parameters of component I7 / NM1 / n18_dnw_ckt on delay1Nominal, and the sum of the influence coefficients of all first sub-parameters of component I7 / PM0 on delay1Nominal are both 0, and so on. According to Table 4, the sum of the influence coefficients of all first sub-parameters on each third preset parameter and the sum of the influence coefficients of all second sub-parameters on each third preset parameter can be obtained.

[0083] Table 4

[0084]

[0085] In an exemplary embodiment, the above embodiment further includes determining whether the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model:

[0086] Using the first parameter value and the second parameter value as independent variables and the third parameter value as the dependent variable, fitting the first parameter value, the second parameter value, and the third parameter value using a first linear regression model to determine a fitting result;

[0087] If the test parameter of the fitting result is greater than or equal to a second preset threshold, determining that the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model;

[0088] If the test parameter of the fitting result is less than the second preset threshold, it is determined that the first parameter value, the second parameter value, and the third parameter value are not suitable for fitting the first linear regression model.

[0089] Since the present disclosure needs to determine the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter, the first parameter value of the first preset parameter and the second parameter value of the second preset parameter are used as independent variables, and the third parameter value of the third preset parameter is used as the dependent variable. The first parameter value, the second parameter value and the third parameter value are fitted using the first linear regression model to obtain a fitting result. The test parameter of the fitting result is used to verify whether the distribution of the first parameter value, the second parameter value and the third parameter value conforms to the distribution of the first linear regression model. The test parameter is determined according to the test method, and the test method includes a significance test, such as an F test, a T test, etc. The second preset threshold is an empirical value. When the test parameter of the fitting result is greater than or equal to the second preset threshold, it indicates that the distribution of the first parameter value, the second parameter value and the third parameter value conforms to the distribution of the first linear regression model, that is, it is determined that the first parameter value, the second parameter value and the third parameter value are suitable for fitting the first linear regression model; when the test parameter of the fitting result is less than the second preset threshold, it indicates that the distribution of the first parameter value, the second parameter value and the third parameter value does not conform to the distribution of the first linear regression model, that is, it is determined that the first parameter value, the second parameter value and the third parameter value are not suitable for fitting the first linear regression model.

[0090] Table 5

[0091]

[0092] In an exemplary embodiment, the above-mentioned display of the influence coefficient further includes:

[0093] In response to the first parameter value, the second parameter value, and the third parameter value being unsuitable for fitting the first linear regression model, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are marked in a preset manner.

[0094] The preset method includes recording the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter as NA. In an example, Table 5 shows the influence coefficient of each first sub-parameter in the first preset parameter on the third preset parameter, and the influence coefficient of each second sub-parameter in the second preset parameter on the third preset parameter. As shown in Table 5, delay1Nominal represents the third preset parameter under normal conditions, delay1C1 represents the third preset parameter under C1 conditions, risetime1Nominal represents the third preset parameter under normal conditions, risetime1C1 represents the third preset parameter under C1 conditions, delay1 and risetime1 represent different third preset parameters, R 2 It represents the goodness of fit of the first linear regression model. The parameters in the first column represent the first sub-parameters in the first preset parameters. It can be seen from Table 5 that these first sub-parameters and the third preset parameters are not suitable for fitting the first linear regression model.

[0095] In an exemplary embodiment of the present disclosure, a device for determining an influence coefficient is provided. Figure 6 is a block diagram of a device for determining an influence coefficient according to an exemplary embodiment. Figure 6 As shown, the device for determining the influence coefficient includes:

[0096] A first acquisition module 601 is configured to acquire a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same components on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after chip simulation;

[0097] The second acquisition module 602 is configured to determine a third parameter value of a third preset parameter according to the first parameter value and the second parameter value; the third preset parameter is related to the first preset parameter and the second preset parameter;

[0098] a fitting module 603 configured to, in response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting the first linear regression model, determine a target fitting result based on the fitting result of the first linear regression model to the first parameter value, the second parameter value, and the third parameter value;

[0099] The determination module 604 is configured to determine the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter according to the target fitting result.

[0100] In an exemplary embodiment, the fitting module 603 is further configured to:

[0101] When the parameter value of the preset parameter of the first linear regression model is greater than or equal to the first preset threshold, the fitting result of the first linear regression model is determined as the target fitting result;

[0102] When the parameter value of the preset parameter of the first linear regression model is less than the first preset threshold value, the first parameter value, the second parameter value and the third parameter value are refitted using the second linear regression model to obtain a fitting result of the second linear regression model, and the fitting result of the second linear regression model is determined as the target fitting result; the fitting ability of the second linear regression model is greater than the fitting ability of the first linear regression model.

[0103] In an exemplary embodiment, the determination module 604 is further configured to:

[0104] Determine the ratio of the coefficient of the first parameter value in the target fitting result to the sum of all coefficients as the influence coefficient of the first preset parameter on the third preset parameter;

[0105] The ratio of the coefficient of the second parameter value in the target fitting result to the sum of all coefficients is determined as the influence coefficient of the second preset parameter on the third preset parameter.

[0106] In an exemplary embodiment, the first preset parameter includes a plurality of first sub-parameters, the second preset parameter includes a plurality of second sub-parameters, and the apparatus for determining the influence coefficient further includes a display module 605 configured to:

[0107] Displaying the influence coefficient of each first sub-parameter on the third preset parameter, and the influence coefficient of each second sub-parameter on the third preset parameter; and / or,

[0108] The sum of the influence coefficients of all first sub-parameters on the third preset parameter and the sum of the influence coefficients of all second sub-parameters on the third preset parameter are displayed.

[0109] In an exemplary embodiment, the display module 605 is further configured to:

[0110] A preset identifier is displayed, where the preset identifier represents an identifier of a linear regression model used when fitting the first parameter value, the second parameter value, and the third parameter value.

[0111] In an exemplary embodiment, the fitting module 603 is further configured to:

[0112] Using the first parameter value and the second parameter value as independent variables and the third parameter value as the dependent variable, fitting the first parameter value, the second parameter value, and the third parameter value using a first linear regression model to determine a fitting result;

[0113] If the test parameter of the fitting result is greater than or equal to a second preset threshold, determining that the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model;

[0114] If the test parameter of the fitting result is less than the second preset threshold, it is determined that the first parameter value, the second parameter value, and the third parameter value are not suitable for fitting the first linear regression model.

[0115] In an exemplary embodiment, the display module 605 is further configured to:

[0116] In response to the first parameter value, the second parameter value, and the third parameter value being unsuitable for fitting the first linear regression model, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are marked in a preset manner.

[0117] Each module in the aforementioned camera device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0118] In an exemplary embodiment, a computer device is provided, including a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any of the above-mentioned shooting methods are implemented.

[0119] In one exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-described photographing methods are implemented. The computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0120] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the steps of any of the above-mentioned shooting directions when executed by a processor.

[0121] refer to Figure 7 , a block diagram of a computer device 700 that can serve as the camera 2 or terminal 1 of the present disclosure will now be described. The computer device 700 includes a computing unit 707, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the computer device 700 may also be stored in the RAM 703. The computing unit 707, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0122] Multiple components within computer device 700 are connected to I / O interface 705, including an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. Input unit 706 can be any type of device capable of inputting information into computer device 700. Input unit 706 can receive input numeric or character information and generate key signal input related to user settings and / or function control of computer device 700, and may include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 707 can be any type of device capable of presenting information, and may include, but is not limited to, a display, speakers, video / audio output terminals, vibrators, and / or printers. Storage unit 708 may include, but is not limited to, magnetic disks and optical disks. Communication unit 709 allows computer device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0123] The computing unit 707 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 707 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 707 performs the various methods and processes described above, such as the shooting method. For example, in some embodiments, the shooting method can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the computer device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 707, one or more steps of the shooting method described above can be performed. Alternatively, in other embodiments, the computing unit 707 can be configured to perform the shooting method by any other suitable means (e.g., via firmware).

[0124] The computer device 700 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-mentioned shooting method.

[0125] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0126] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for determining an influence coefficient, characterized in that: The method comprises: Obtaining a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same components on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after the chip simulation; determining, based on the first parameter value and the second parameter value, a third parameter value of a third preset parameter, wherein the third preset parameter represents a calculation result of a preset expression in the integrated circuit, the preset expression being used to calculate a voltage and a current of a branch in the integrated circuit; and the third preset parameter is related to the first preset parameter and the second preset parameter; In response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting of a first linear regression model, determining a target fitting result based on a fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value; According to the target fitting result, influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are determined.

2. The method for determining the influence coefficient according to claim 1, wherein: The determining a target fitting result according to the fitting result of the first parameter value, the second parameter value, and the third parameter value by the first linear regression model includes: When a parameter value of a preset parameter of the first linear regression model is greater than or equal to a first preset threshold, determining the fitting result of the first linear regression model as the target fitting result; When the parameter value of the preset parameter of the first linear regression model is less than the first preset threshold, the first parameter value, the second parameter value, and the third parameter value are refitted using a second linear regression model to obtain a fitting result of the second linear regression model, and the fitting result of the second linear regression model is determined as the target fitting result; the fitting ability of the second linear regression model is greater than the fitting ability of the first linear regression model.

3. The method for determining the influence coefficient according to claim 2, wherein: The determining, according to the target fitting result, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter includes: Determining a ratio of a coefficient of the first parameter value to a sum of all coefficients in the target fitting result as an influence coefficient of the first preset parameter on the third preset parameter; The ratio of the coefficient of the second parameter value in the target fitting result to the sum of all coefficients is determined as the influence coefficient of the second preset parameter on the third preset parameter.

4. The method for determining the influence coefficient according to claim 3, wherein: The first preset parameter includes a plurality of first sub-parameters, the second preset parameter includes a plurality of second sub-parameters, and the method further includes: displaying the influence coefficient of each first sub-parameter on the third preset parameter, and the influence coefficient of each second sub-parameter on the third preset parameter; and / or, The sum of the influence coefficients of all the first sub-parameters on the third preset parameter and the sum of the influence coefficients of all the second sub-parameters on the third preset parameter are displayed.

5. The method for determining the influence coefficient according to claim 4, characterized in that: The method further comprises: A preset identifier is displayed, where the preset identifier represents an identifier of a linear regression model used when fitting the first parameter value, the second parameter value, and the third parameter value.

6. The method for determining the influence coefficient according to claim 1, wherein: The method further comprises: Using the first parameter value and the second parameter value as independent variables and the third parameter value as a dependent variable, fitting the first parameter value, the second parameter value, and the third parameter value using the first linear regression model to determine a fitting result; If the test parameter of the fitting result is greater than or equal to a second preset threshold, determining that the first parameter value, the second parameter value, and the third parameter value are suitable for fitting the first linear regression model; If the test parameter of the fitting result is less than the second preset threshold, it is determined that the first parameter value, the second parameter value, and the third parameter value are not suitable for fitting the first linear regression model.

7. The method for determining the influence coefficient according to claim 6, wherein: The method further comprises: In response to the first parameter value, the second parameter value, and the third parameter value being unsuitable for fitting the first linear regression model, the influence coefficients of the first preset parameter and the second preset parameter on the third preset parameter are marked in a preset manner.

8. A device for determining an influence coefficient, characterized in that: The device comprises: A first acquisition module is configured to acquire a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, wherein the first preset parameter represents a random deviation parameter between the same components on the same wafer area after chip simulation, and the second preset parameter represents a process angle deviation parameter between different wafers after the chip simulation; a second acquisition module configured to determine a third parameter value of a third preset parameter based on the first parameter value and the second parameter value, wherein the third preset parameter represents a calculation result of a preset expression in the integrated circuit, the preset expression being used to calculate a voltage and a current of a branch in the integrated circuit; and the third preset parameter is related to the first preset parameter and the second preset parameter; a fitting module configured to, in response to the first parameter value, the second parameter value, and the third parameter value being suitable for fitting a first linear regression model, determine a target fitting result based on a fitting result of the first linear regression model for the first parameter value, the second parameter value, and the third parameter value; A determination module is configured to determine, based on the target fitting result, an influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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