Data selection method, system and device for extracting parameters of integrated circuit device model

Abstract

The application discloses a data selection method, system and device for extracting parameters of an integrated circuit device model, wherein test data sets of the integrated circuit device are received, at least three different screening conditions are configured, and a plurality of bias attributes are set for each screening condition; the screening conditions and the bias attributes are filled in the form of labels to generate a mapping table; after a plurality of test data are classified once by fixed screening according to the screening conditions, the bias attributes are used for secondary self-defined screening, the screening results are mapped to corresponding labels of the mapping table, the association relationship of self-defined test data in different screening results is constructed, and the self-defined test data of each screening is stored in the form of a set; the device model of the integrated circuit device is fitted to perform conditional instantiation, and the bias attributes are adjusted to achieve the selection purpose of the data. The application realizes flexible selection of bias data, and does not increase the complexity of process setting.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of integrated circuit computer-aided design technology, and in particular, to a data selection method, system and device for extracting parameters of an integrated circuit device model. BACKGROUND

[0002] The continuous development of semiconductor and integrated circuit technology makes the importance of integrated circuit computer-aided design (CAD) or electronic design automation (EDA) platforms more prominent. One of the basic functions of the EDA platform is the extraction of parameters of a device model, that is, based on some standard device models, the model parameters of a semiconductor device manufactured by a specific integrated circuit process are extracted. After the model parameters are extracted, combined with the corresponding standard device model, various working characteristics of the semiconductor device can be described in a mathematical way, so as to be used for device simulation in subsequent circuit design.

[0003] The BSIM model is a metal-oxide field effect transistor (MOSFET) model developed by the University of California, Berkeley, which is suitable for digital and analog circuit design and simulation. In actual parameter extraction operations, various BSIM models (such as BSIM4, BSIM-Bulk, BSIM-CMG, etc.) corresponding to actual MOSFET devices can be selected to process test data (such as I-V curve C-V curve, etc. of MOSFETs of different sizes) of MOSFET devices, and model parameters of MOSFET devices can be extracted.

[0004] The existing publicly disclosed technical solutions for extracting device model parameters of integrated circuit devices are used to solve the defects that a large amount of test data is processed during model parameter extraction, which consumes a large amount of time and computing resources, and to improve the accuracy of model parameters. However, in actual application process, it is found that the technical solution: since the behavior region reflected by the device data is accurately divided and selected by analysis, it is one of the key links in the process strategy of integrated circuit device model parameter extraction, but due to the different links and strategies of the device data according to different extraction processes, there are many flexible bias condition setting requirements, in order to avoid the problem of large amount of calculation caused by repeated execution of data extraction steps, the reusable data selection method will become the process strategy required for realizing the extraction of device model parameters, so it is necessary to improve the data selection for extracting parameters of integrated circuit device models. SUMMARY

[0005] The embodiment of the present application provides a data selection method, system and device for extracting parameters of an integrated circuit device model, and solves the technical problem that the data extraction step is repeatedly executed according to the bias condition setting requirement in the process of extracting parameters of a device model in the prior art, realizes that a mapping table with a bias attribute is preconfigured, and when the fitting operation of the device model is performed, the bias attribute in the mapping table can be directly adjusted to realize the fitting of the device model by the self-defined test data set of different screening results, and the technical problem of repeatedly executing the data extraction operation is avoided.

[0006] In a first aspect, the embodiment of the present application provides a data selection method for extracting parameters of an integrated circuit device model, and the method comprises the following steps:

[0007] Receiving a test data set of the integrated circuit device, wherein the test data set comprises a plurality of test data obtained by testing the integrated circuit device under different test conditions;

[0008] In response to a user-defined condition filtering setting interface configured in a visual operation interface, at least three different filtering conditions are configured through the condition filtering setting interface, and a plurality of bias attributes are set for each filtering condition; the filtering conditions and the bias attributes are filled in the form of labels to generate a mapping table;

[0009] In response to a preconfigured database, after the test data is obtained, the plurality of test data is once fixedly screened and classified according to the filtering conditions, and then the bias attributes are used for secondary self-defined screening, the screening results are mapped to corresponding labels of the mapping table, the association relationship of the self-defined test data in different screening results is constructed, and the self-defined test data of each screening is stored in the form of a set;

[0010] The device model of the integrated circuit device is fitted by using the self-defined test data set, the condition instantiation of the self-defined test data is performed according to the association relationship, and the self-defined test data is selected by adjusting the bias attributes.

[0011] Further, after the filtering conditions and the bias attributes are filled in the form of labels to generate the mapping table, a bias selection template is generated based on the condition variables of the bias attributes set or selected by the user, so that when the condition instantiation of the self-defined test data is performed, the bias attributes can be self-defined and adjusted.

[0012] Further, in the mapping table, different labels correspond to different filtering conditions and different bias attributes.

[0013] Further, in the process of fitting the device model, the device model maps two-dimensional fitting of any two screened custom test data sets and three-dimensional fitting of three or more screened custom test data sets in the visualization operation interface.

[0014] Further, the condition screening setting interface includes fixedly configured extraction condition fields, such that after receiving user input, one screening condition is set through each extraction condition field.

[0015] Further, each screening condition in the condition screening setting interface includes a plurality of bias condition areas, and each bias condition area sets one bias attribute.

[0016] Further, when receiving user input, at least one of the plurality of bias condition areas is selected to set or select a bias condition variable in the bias attribute.

[0017] In the second aspect, the embodiments of the present application provide a data selection system for extracting parameters of an integrated circuit device model, which adopts the method in any one of the first aspect, and the system comprises:

[0018] A data receiving module configured to receive a test data set of the integrated circuit device, wherein the test data set includes a plurality of test data obtained by testing the integrated circuit device under different test conditions;

[0019] A form generating module configured to, in response to a user-defined condition screening setting interface being configured in a visualization operation interface, configure at least three different screening conditions through the condition screening setting interface, and set a plurality of bias attributes for each screening condition; fill the screening conditions and the bias attributes in the form of labels to generate a mapping form;

[0020] A data screening module configured to, in response to a database being preconfigured, after obtaining the test data, perform a fixed screening classification on the plurality of test data according to the screening conditions, and then perform a secondary custom screening by using the bias attributes, map the screening results to corresponding labels of the mapping form, construct an association relationship of custom test data in different screening results, and store the custom test data of each screening in the form of a set;

[0021] A model fitting module configured to fit a device model of the integrated circuit device by using the custom test data set, conditionally instantiate the custom test data according to the association relationship, and realize selection of the custom test data by adjusting the bias attributes.

[0022] Thirdly, embodiments of this application provide a data selection device for extracting parameters from an integrated circuit device model. The device includes a non-transitory computer storage medium storing one or more executable instructions. When the one or more executable instructions are executed by a processor, they perform the method described in any one of the first aspects.

[0023] The technical solutions provided in this application embodiment have at least the following technical effects:

[0024] By employing a user-defined filtering settings interface to construct a mapping form, bias attributes can be dynamically set even with fixed filtering conditions. This allows for the acquisition of custom test datasets with varying bias requirements within the generated mapping form, enabling the fitting of device models and the extraction of data for parameter extraction. Customizing bias attributes through the mapping form achieves flexible selection and reusability of bias conditions, while maintaining flexibility in bias condition selection without increasing the complexity of the workflow. Attached Figure Description

[0025] Figure 1 This is a flowchart of the data selection method for extracting parameters from the integrated circuit device model in Embodiment 1 of this application;

[0026] Figure 2 This is the user-defined condition filtering settings interface in Embodiment 1 of this application;

[0027] Figure 3 This is the interface for setting the filtering criteria in Embodiment 1 of this application;

[0028] Figure 4 This is the bias attribute setting interface in Embodiment 1 of this application;

[0029] Figure 5 This is a schematic interface for setting the bias attribute in Embodiment 1 of this application;

[0030] Figure 6 For corresponding Figure 5 Two-dimensional fitting curve plot corresponding to the bias attribute;

[0031] Figure 7 This is a schematic interface for adjusting the bias attribute in Embodiment 1 of this application;

[0032] Figure 8 for Figure 6 and Figure 7 Comparison of the corresponding two-dimensional fitting curves;

[0033] Figure 9 This is a block diagram of the data selection system for extracting parameters from the integrated circuit device model in Embodiment 2 of this application. DETAILED DESCRIPTION

[0034] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure.

[0035] Since the parameter extraction process of the device model of the integrated circuit device is performed by each integrated circuit manufacturing company according to the integrated circuit process that it can provide, it can be known that the actual performance of the integrated circuit device depends on the corresponding integrated circuit process. However, different operators will perform differentiated parameter extraction operations according to personal work experience and software use habits, company self-defined parameter extraction requirements, and other factors.

[0036] Therefore, in order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings of the specification and the specific embodiments.

[0037] Embodiment one

[0038] The embodiment of the present application provides a data selection method for extracting parameters of an integrated circuit device model. Through the method, an operator is assisted to increase a parameter extraction condition setting function in a parameter extraction software of a device model, and a bias design is set in the function to perform a screening purpose according to different bias requirements.

[0039] The method includes the following steps.

[0040] Step S100: receiving a test data set of the integrated circuit device, wherein the test data set includes a plurality of test data obtained by testing the integrated circuit device under different test conditions. The test data includes but is not limited to current, voltage, and capacitance.

[0041] Step S200: in response to a user-defined condition screening setting interface configured in a visual operation interface, configuring at least three different screening conditions through the condition screening setting interface, and setting a plurality of bias attributes for each screening condition; filling the screening conditions and the bias attributes in the form of labels to generate a mapping table.

[0042] Step S300: in response to being pre-configured with the database, after obtaining the test data, performing a fixed screening classification on the test data according to the screening condition, then performing a secondary self-defined screening using the bias attribute, mapping the screening result to the corresponding label of the mapping table, constructing the association relationship of the self-defined test data in different screening results, and storing the self-defined test data of each screening in a set form.

[0043] Step S400: fitting the device model of the integrated circuit device using the self-defined test data set, performing conditional instantiation of the self-defined test data according to the association relationship, and realizing selection of the self-defined test data by adjusting the bias attribute.

[0044] It should be further understood that the data selection method provided in the embodiment is designed based on the premise of extracting parameters of the integrated circuit device model, that is, the integrated circuit device model needs to be determined in advance, and then the data selection in the model extraction parameter is considered, so that the selected data is used for various integrated circuit devices and the corresponding models. In some embodiments, the integrated circuit device can be, but is not limited to, the following devices: MOSFET transistor, silicon-on-insulator transistor (SOI), fin field effect transistor (FinFET), bipolar transistor (BJT), heterojunction transistor (HBT), thin film transistor (TFT), metal semiconductor contact field effect transistor (MESFET), diode, resistor or inductor, etc. The determined device model can be, but is not limited to, BSIM3, BSIM4, BSIM6, BSIM-CMG, BSIM-IMG, BSIMSOI, UTSOI, HiSIM2, HiSIM_HV, PSP, GP-BJT or RPITFT. For example, for MOSFET transistor, the corresponding device model can be BSIM3, BSIM4, BSIM6 or other known standard model or non-standard model. It can be understood that the above device model is only exemplary, and in actual application, the model corresponding to the integrated circuit device can be selected as needed. Based on the fact that MOSFET transistor is one of the most commonly used devices in integrated circuits, the integrated circuit device in the embodiment is taken as an example for illustration. However, those skilled in the art can understand that the application of the present application is not limited thereto.

[0045] After selecting and determining the applicable device model, in order to extract the model parameters, it is also necessary to provide test data corresponding to the integrated circuit device. The test data can be obtained by testing the integrated circuit device under different test conditions. The various test conditions described in this embodiment can be combined to form new test conditions. For example, test conditions can be different dimensions of the integrated circuit device (e.g., different channel lengths and widths), different voltage bias conditions (e.g., bias voltage Vbs between the body and source, different source and drain voltages Vds, etc.), different temperature conditions, etc. Different types of test conditions can be combined into a new set of test conditions and used to describe the physical characteristics and test environment of the integrated circuit device under test, such as the device's channel length, width, and body bias voltage, etc. It should be noted that the integrated circuit device in this embodiment does not refer to a specific physical device, but rather to a general term for a class of devices fabricated using the same integrated circuit manufacturing process. For example, two integrated circuit devices fabricated using the same process but differing only in channel width can be considered the same integrated circuit device.

[0046] Therefore, testing an integrated circuit device under each set of test conditions can generate corresponding test data, which can be one or more of current, voltage, capacitance, or other derived electrical parameters. Thus, test data obtained under multiple sets of test conditions can constitute a test dataset. In some other embodiments, the test data can vary or be adjusted according to the test conditions and test requirements, and this application is not limited thereto. For example, derived electrical parameters may include parameters such as Idin, saturation leakage current Idsat, maximum transconductance maxGm, Vtlin, saturation threshold voltage Vtsat, and Vtgm, and may also include electrical output parameters such as Gm and Gds. Further details about these parameters can be found in the BSIM model or other models. These electrical parameters may vary with voltage.

[0047] Reference Appendix Figures 2-8 As shown, in this embodiment, the filtering conditions and bias attributes are filled into the generated mapping form in the form of tags. This forms a bias selection template based on the bias attributes set or selected by the user input, so that the bias attributes can be customized and adjusted when instantiating the conditions of the custom test data. Furthermore, in the mapping form, different tags correspond to different filtering conditions and different bias attributes, and one filtering condition can be configured with multiple different bias attributes.

[0048] The condition screening setting interface in the embodiment includes fixedly configured extraction condition fields, such that after receiving user input, one screening condition is set through each extraction condition field. Each screening condition in the condition screening setting interface includes multiple bias condition areas, and each bias condition area is configured to set one bias attribute. When receiving user input, at least one of the multiple bias condition areas is selected to set or select a bias condition variable in the bias attribute.

[0049] In the embodiment, after fixedly configured screening conditions are given, the bias attribute is set. For example, the fixed screening condition is the type of test data, for example, the test data in the test data set is divided according to the data type, such that the test data is classified and screened for the first time through the screening condition, and then different bias attributes are selected for different types of test data. In the embodiment, the screening condition is the data type, and the screening condition can be the current, voltage, capacitance, and the like. The bias attribute can be the bias selection based on different types, such as step (Step), points (Points), reference point (Reference), reference offset (Offset), and the like.

[0050] In the embodiment, the user-defined condition screening setting interface is adopted, that is, the screening condition and the bias attribute are both self-defined configurations at the initial setting. After the fixed screening condition configuration is completed, the bias attribute can be dynamically updated, the bias condition can be adjusted according to the demand, and the flexible selection of the bias condition is realized, so as to flexibly provide multiple types of bias setting related options for each integrated circuit device port. Therefore, it can be known that the bias attribute is flexibly adjusted to achieve the purpose of data selection, so as to meet the test data acquisition of more bias requirements and reduce the difficulty of the operator in the bias selection operation.

[0051] In this embodiment, the received test data is screened once by using the screening condition, which is equivalent to the screening condition in the mapping form being fixed after the first custom configuration, and the screening condition does not change regardless of the change of the bias attribute. Further, data selection is performed by using different bias attribute settings. In this embodiment, voltage bias is used, so the bias condition in the bias attribute is a voltage bias condition. In the drawings, Vg, Vd, and Vb respectively represent the gate (Gate), drain (Drain), and substrate (Substrate) voltages of a device. In the bias attribute setting in this embodiment, a voltage bias region setting is also included, for example, when the device data is measured, the voltage scanning region / range is obtained, such as: if Vg1 < Vg2, the voltage bias condition set of [Vg1, Vg2] is taken as a voltage bias region; the bias attribute also includes a bias option for the user to set the bias condition (voltage bias condition) option; the bias attribute also includes a bias selection, and the operator sets and selects the appropriate bias condition (voltage bias condition) through the bias option according to his own habit / policy.

[0052] Based on the fixed screening condition, a plurality of custom test data sets are obtained, and the screening condition is adapted to the fitting operation of the device model. When fitting the device model, the device model maps the two-dimensional fitting of any two screened custom test data sets and the three-dimensional fitting of three or more screened custom test data sets in the visualization operation interface. Since the data in the custom test data set is realized by conditional instantiation through the association relationship, the operator can select a group of data that meets the test requirements according to the fitting effect.

[0053] And according to the data selection method for extracting parameters of an integrated circuit device model provided in the present application, before performing the calculation processing of the device model parameter extraction, the operator configures the screening condition and the bias attribute through the user-defined condition screening setting interface, and completes the at least three-dimensional data fitting of the device model by using at least three different screening conditions. Of course, when two screening conditions are used, a two-dimensional fitting curve is directly obtained. The operator can select or set various screening conditions for parameter extraction, and can save a group of selected or set screening conditions. Then, different bias attributes are adjusted according to the fixed screening condition to select data with different bias requirements. Similarly, after performing a data selection operation, if the operator wants to use the same screening condition, the operator can call the previous screening condition before performing the next operation. Different operators can use a certain parameter extraction processing, and only need to obtain a group of pre-saved parameter extraction conditions, so that the standardization and reusability of the parameter extraction processing are possible, and the flexibility of the condition setting is not reduced. The operator can still adjust various conditions of the parameter extraction according to personal needs.

[0054] Example 2

[0055] Reference Appendix Figure 9 As shown, this application provides a data selection system for extracting parameters from an integrated circuit device model, which employs the method described in any one of the embodiments in Embodiment 1. The system includes the following modules.

[0056] The data receiving module 100 is configured to receive a test dataset of the integrated circuit device, wherein the test dataset includes several types of test data obtained by testing the integrated circuit device under different test conditions.

[0057] The form generation module 200 is configured to respond to a user-defined condition filtering settings interface in the visual operation interface, configure at least three different filtering conditions through the condition filtering settings interface, and set several bias attributes for each filtering condition; fill in the filtering conditions and the bias attributes in the form of tags to generate a mapping form.

[0058] The data filtering module 300 is configured to respond to a pre-configured database, and after obtaining the test data, perform a fixed filtering and classification of the test data according to the filtering conditions, and then perform a second custom filtering using the bias attribute, mapping the filtering results to the corresponding labels of the mapping form, constructing the association relationship of custom test data in different filtering results, and storing the custom test data of each filtering in the form of a set.

[0059] The model fitting module 400 is configured to fit the device model of the integrated circuit device using the custom test dataset, perform conditional instantiation of the custom test data according to the correlation, and select the custom test data by adjusting the bias attribute.

[0060] Example 3

[0061] This application provides a data selection device for extracting parameters from an integrated circuit device model. The device includes a non-transitory computer storage medium storing one or more executable instructions. After the one or more executable instructions are executed by a processor, the following method steps in Embodiment 1 are performed.

[0062] Step S100: Receive the test dataset of the integrated circuit device, which includes several types of test data obtained by testing the integrated circuit device under different test conditions.

[0063] Step S200: in response to the user-defined condition filtering setting interface configured in the visualization operation interface, configuring at least three different filtering conditions through the condition filtering setting interface, and setting a plurality of bias attributes for each filtering condition; filling the filtering conditions and the bias attributes in the form of labels to generate a mapping table.

[0064] Step S300: in response to the database being pre-configured, after the test data is acquired, performing a fixed filtering classification on the plurality of test data according to the filtering conditions, then performing a secondary user-defined filtering by using the bias attributes, mapping the filtering results to the corresponding labels of the mapping table, constructing the association relationship of the user-defined test data in different filtering results, and storing the user-defined test data of each filtering in the form of a set.

[0065] Step S400: fitting the device model of the integrated circuit device by using the user-defined test data set, performing condition instantiation of the user-defined test data according to the association relationship, and realizing the selection of the user-defined test data by adjusting the bias attributes.

[0066] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0067] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The means for performing the functions specified in a flow or multiple flows and / or blocks.

[0068] These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0069] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operations steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flow Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0070] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to encompass within their scope all such variations and modifications as are included within the scope of the application.

[0071] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A data selection method for extracting parameters of an integrated circuit device model, characterized by, The method comprises: receiving a test data set of the integrated circuit device, the test data set comprising a plurality of test data obtained by testing the integrated circuit device under different test conditions; in response to a user-defined condition filtering setting interface configured in the visualization operation interface, configuring at least three different filtering conditions through the condition filtering setting interface, and setting a plurality of bias attributes for each filtering condition; filling the filtering conditions and the bias attributes in the form of labels to generate a mapping form; in response to a preconfigured database, after obtaining the test data, performing a fixed filtering classification on the plurality of test data according to the filtering conditions, then performing a secondary customized filtering using the bias attributes, mapping the filtering results to the corresponding labels of the mapping form, constructing the association relationship of the customized test data in different filtering results, and storing the customized test data of each filtering in the form of a set; using the customized test data set to fit the device model of the integrated circuit device, performing condition instantiation of the customized test data according to the association relationship, and achieving the selection purpose of the customized test data by adjusting the bias attributes; after the filtering conditions and the bias attributes are filled in the form of labels to generate the mapping form, a bias selection template is formed based on the condition variables of the bias attributes set or selected by the user, so that the bias attributes can be customized and adjusted when the condition instantiation of the customized test data is performed; when fitting the device model, the device model maps the two-dimensional fitting of any two sets of customized test data after filtering and the three-dimensional fitting of three or more sets of customized test data after filtering in the visualization operation interface.

2. The data selection method for extracting parameters of an integrated circuit device model according to claim 1, wherein, The condition filtering setting interface comprises a fixedly configured extraction condition field, so that after receiving user input, a filtering condition is set through each extraction condition field.

3. The data selection method for integrated circuit device model extraction parameter according to claim 2, wherein, Each filtering condition in the condition filtering setting interface comprises a plurality of bias condition areas, and each bias condition area is configured to set one bias attribute.

4. The data selection method for integrated circuit device model extraction parameter according to claim 3, wherein, When receiving user input, at least one of the plurality of bias condition areas is selected to set or select a bias condition variable in the bias attribute.

5. A data selection system for extracting parameters of an integrated circuit device model, using the method according to any one of claims 1 to 4, characterized in that The system comprises: a data receiving module configured to receive a test data set of the integrated circuit device, the test data set comprising a plurality of test data obtained by testing the integrated circuit device under different test conditions; a form generation module configured to, in response to a user-defined condition filtering setting interface configured in the visualization operation interface, configure at least three different filtering conditions through the condition filtering setting interface, and set a plurality of bias attributes for each filtering condition; fill the filtering conditions and the bias attributes in the form of labels to generate a mapping form; The data screening module is configured to, in response to being preconfigured with a database, after obtaining the test data, perform a fixed screening classification on the test data according to the screening condition, then perform a secondary self-defined screening by using the bias attribute, map the screening result to a corresponding label of the mapping table, construct an association relationship of the self-defined test data in different screening results, and store the self-defined test data of each screening in a set form. The model fitting module is configured to fit a device model of the integrated circuit device by using the self-defined test data set, conditionally instantiate the self-defined test data according to the association relationship, and realize selection of the self-defined test data by adjusting the bias attribute.

6. A data selection device for extracting parameters of an integrated circuit device model, characterized by The apparatus includes a non-transitory computer storage medium having stored thereon one or more executable instructions that, when executed by a processor, perform the method of any one of claims 1-4.

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