WO2023213094A1 - 应用于集成电路器件的数据区域动态选取方法、系统、设备和计算机可读存储介质 - Google Patents

应用于集成电路器件的数据区域动态选取方法、系统、设备和计算机可读存储介质 Download PDF

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WO2023213094A1
WO2023213094A1 PCT/CN2022/142570 CN2022142570W WO2023213094A1 WO 2023213094 A1 WO2023213094 A1 WO 2023213094A1 CN 2022142570 W CN2022142570 W CN 2022142570W WO 2023213094 A1 WO2023213094 A1 WO 2023213094A1
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integrated circuit
data
bias condition
data set
dynamic
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PCT/CN2022/142570
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English (en)
French (fr)
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梁汉成
石凯
刘志宏
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上海概伦电子股份有限公司
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Publication of WO2023213094A1 publication Critical patent/WO2023213094A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods

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  • the present invention relates to the technical field of integrated circuit testing. Specifically, it discloses a data area dynamic selection method, system, equipment and computer-readable storage medium applied to integrated circuit devices.
  • the present invention provides a data area dynamic selection method, system, equipment and computer-readable storage medium applied to integrated circuit devices.
  • the first aspect of this application provides a method for dynamic selection of data areas applied to integrated circuit devices, which may specifically include:
  • test data set and/or simulation data set corresponding to the integrated circuit device Obtain the test data set and/or simulation data set corresponding to the integrated circuit device
  • bias condition variables associated with the integrated circuit device, and the bias condition variables are associated with the data items to be tested and/or the data items to be simulated of the integrated circuit device;
  • test operations are performed based on the dynamic test data set and/or simulation operations are performed based on the dynamic simulation data set.
  • the bias condition variable includes several first characteristic points of the data item to be measured;
  • the first characteristic points may include:
  • the maximum voltage value of the first test point The maximum voltage value of the first test point, the minimum voltage value of the first test point, the zero voltage value of the first test point, the saturation voltage value of the first test point and the threshold voltage value of the first test point.
  • the second characteristic points may include:
  • the maximum voltage value of the second test point The maximum voltage value of the second test point, the minimum voltage value of the second test point, the zero voltage value of the second test point, the saturation voltage value of the second test point and the threshold voltage value of the second test point.
  • the bias condition corresponding to the bias condition variable is determined based on several selected first feature points and/or several selected second feature points;
  • the bias condition includes a data interval formed by a number of selected first feature points and/or second feature points.
  • user instructions include manual instructions and machine instructions
  • Data area dynamic selection methods also include:
  • the user instruction includes at least one machine instruction, obtain the historical offset condition variable corresponding to the data item to be tested and/or the data item to be simulated;
  • At least one bias condition variable corresponding to the machine instruction is obtained through machine learning.
  • the dynamic test data set dynamically changes according to changes in the test data set
  • Dynamic simulation data sets dynamically change based on changes in the simulation data set.
  • the second aspect of the present application provides a system for dynamic selection of data areas applied to integrated circuit devices, which is applied to the method for dynamically selecting data areas applied to integrated circuit devices provided in the first aspect, including:
  • An acquisition unit used to acquire the test data set and/or simulation data set corresponding to the integrated circuit device
  • the first setting unit is used to obtain a number of bias condition variables associated with the integrated circuit device according to user instructions.
  • the bias condition variables are associated with the data items to be tested and/or the data items to be simulated of the integrated circuit device;
  • the second setting unit is used to obtain the bias condition corresponding to the bias condition variable according to the user instruction
  • a generation unit used to generate a custom selection model associated with the data item to be tested and/or the data item to be simulated based on the bias condition variable and the bias condition;
  • the selection unit is used to select the corresponding dynamic test data set in the test data set according to the custom selection model.
  • the processing unit is configured to perform test operations based on the dynamic test data set and/or perform simulation operations based on the dynamic simulation data set for the integrated circuit device.
  • the third aspect of this application provides a data area dynamic selection device applied to integrated circuit devices, including:
  • Memory used to store computer programs
  • the processor is configured to implement the data area dynamic selection method for integrated circuit devices provided by the first aspect when executing a computer program.
  • the fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program.
  • the computer program is executed by a processor, the method provided by the first aspect is applied to an integrated circuit device. Dynamic selection method of data area.
  • Figure 1 shows a schematic flow chart of a method for dynamically selecting a data area applied to an integrated circuit device according to an embodiment of the present application
  • Figure 2 shows a partial schematic diagram of a user interface for selecting bias condition variables according to an embodiment of the present application
  • Figure 3 shows a schematic flow chart of a method for dynamically selecting a data area based on machine instructions according to an embodiment of the present application
  • Figure 4 shows a schematic structural diagram of a data area dynamic selection system applied to an integrated circuit device according to an embodiment of the present application
  • Figure 5 shows a schematic structural diagram of a data area dynamic selection device applied to an integrated circuit device according to an embodiment of the present application
  • Figure 6 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.
  • the term “include” and its variations mean an open inclusion, ie, "including but not limited to.” Unless otherwise stated, the term “or” means “and/or”. The term “based on” means “at least regionally based on”. The terms “one example embodiment” and “an embodiment” mean “at least one example embodiment.” The term “another embodiment” means “at least one additional embodiment”. The terms “first,” “second,” etc. may refer to different or the same object. Other explicit and implicit definitions may be included below.
  • this application provides a dynamic selection method, system, equipment and computer-readable storage medium for data areas applied to integrated circuit devices.
  • the strict dependence on the original data set in the data area selection process can be eliminated, simplifying the complexity of data area selection for different integrated circuit devices, and improving the versatility and reuse of data area selection. sex.
  • the technical solutions provided by this application will be explained and described below with reference to examples.
  • FIG. 1 shows a schematic flowchart of a method for dynamically selecting a data area applied to an integrated circuit device.
  • this dynamic selection method of data area applied to integrated circuit devices can specifically include:
  • Step 101 Obtain the test data set and/or simulation data set corresponding to the integrated circuit device.
  • Step 102 According to user instructions, obtain several bias condition variables associated with the integrated circuit device. Wherein, the bias condition variable is associated with the data item to be tested and/or the data item to be simulated of the integrated circuit device.
  • Step 103 According to user instructions, obtain the bias condition corresponding to the bias condition variable.
  • the above-mentioned steps 102 and 103 can be executed synchronously in the actual application process.
  • the user can be provided with a setting interface for selecting conditions.
  • the setting interface can include drop-down menu options for multiple bias condition variables. The user can customize it as needed. Enter the required selection bias condition variables and related bias conditions.
  • Step 104 Generate a custom selection model associated with the data item to be tested and/or the data item to be simulated according to the bias condition variable and the bias condition.
  • Step 105 Select the corresponding dynamic test data set in the test data set according to the custom selection model; and/or select the corresponding dynamic simulation data set in the simulation data set according to the custom selection model.
  • Step 106 For the integrated circuit device, perform a test operation according to the dynamic test data set and/or perform a simulation operation according to the dynamic simulation data set.
  • the bias condition variable includes several first characteristic points of the data item to be measured; and/or several second characteristic points of the data item to be simulated.
  • the data item to be tested or the data to be simulated can be The item is set as a voltage parameter, then when the data item to be measured includes the voltage of several first test points associated with the integrated circuit device in the integrated circuit, the first characteristic point may include the maximum voltage value of the first test point, the first The minimum voltage value of the test point, the zero voltage value of the first test point, the saturation voltage value of the first test point and the threshold voltage value of the first test point.
  • the second characteristic points may include: the maximum voltage value of the second test point, the minimum voltage value of the second test point , the zero voltage value of the second test point, the saturation voltage value of the second test point and the threshold voltage value of the second test point.
  • FIG. 2 shows a schematic diagram of a user interface for selecting bias condition variables. It can be seen that when setting the bias condition variables for the voltage parameters of a certain integrated circuit device, Vg Reference, Vd Reference and Vb Reference can be selected as “anchor points" respectively. As shown in Figure 2, in the user interface, the specific variable information of the above-mentioned "anchor point" can be checked through the drop-down menu.
  • the bias condition corresponding to the bias condition variable may be determined based on selected first feature points and/or several second feature points.
  • the bias condition may include a data interval formed by a number of selected first feature points and/or second feature points.
  • the user instructions can include both manual instructions and machine instructions.
  • the user instructions include machine instructions, it means that the data area dynamic selection method provided by the present application can be applied to automatic or machine instructions. Semi-automatic model parameter extraction process strategy.
  • Figure 3 shows a schematic flow chart of a method for dynamically selecting a data area based on machine instructions. Specifically, as shown in Figure 3, it may include:
  • Step 301 Store the bias condition variable corresponding to the data item to be tested and/or the data item to be simulated.
  • Step 302 If the user instruction includes at least one machine instruction, obtain the historical bias condition variable corresponding to the data item to be tested and/or the data item to be simulated.
  • Step 303 According to the historical bias condition variables, obtain at least one bias condition variable corresponding to the machine instruction through machine learning.
  • the data items to be tested and/or the data items to be simulated can be determined through machine learning and other methods according to the characteristics of the data itself and historical bias condition variables. The current bias condition variable of the data item is confirmed.
  • the dynamic test data set can dynamically change according to changes in the test data set; and/or the dynamic simulation data set can dynamically change according to changes in the simulation data set.
  • the above dynamic selection method of data area applied to integrated circuit devices can be applied to current BSIMProplus TM and SDEP products.
  • BSIMProplus TM is the industry's leading SPICE modeling platform for semiconductor devices. In its more than 20 years of product history, it has been the leader in the global SPICE modeling market and technology and is used by more than 100 leading integrated circuit manufacturing and technology companies around the world. Design companies adopt SPICE as a standard modeling tool. Based on its integrated parallel SPICE engine, BSIMProplus TM provides the industry's most powerful fully integrated SPICE modeling platform, which can be used for SPICE modeling of various device characteristics of various semiconductor devices from low frequency to high frequency, including electrical characteristic testing, Device model automatic parameter extraction and optimization, model verification, etc. The technical solution provided by this application can achieve seamless connection with the above-mentioned existing platforms.
  • FIG. 4 shows a dynamic selection system for data areas applied to integrated circuit devices, which is applied to the dynamic selection method for data areas applied to integrated circuit devices provided in the previous embodiments. Specifically, it can be include:
  • the acquisition unit 001 is used to acquire the test data set and/or simulation data set corresponding to the integrated circuit device.
  • the first setting unit 002 is configured to obtain a number of bias condition variables associated with the integrated circuit device according to user instructions.
  • the bias condition variables are associated with data items to be tested and/or data items to be simulated of the integrated circuit device.
  • the second setting unit 003 is used to obtain the bias condition corresponding to the bias condition variable according to user instructions.
  • the generation unit 004 is configured to generate a customized selection model associated with the data item to be tested and/or the data item to be simulated according to the bias condition variable and the bias condition.
  • the selection unit 005 is used to select the corresponding dynamic test data set in the test data set according to the custom selection model; and/or select the corresponding dynamic simulation data set in the simulation data set according to the custom selection model.
  • the processing unit 006 is configured to perform test operations based on the dynamic test data set and/or perform simulation operations based on the dynamic simulation data set for the integrated circuit device.
  • a device for dynamically selecting data areas applied to integrated circuit devices may include:
  • Memory used to store computer programs
  • a processor configured to implement the steps of the image straightening method described in the technical solution of this application when executing a computer program.
  • FIG. 5 shows a schematic structural diagram of a data area dynamic selection device applied to an integrated circuit device according to some embodiments of the present application.
  • the electronic device 600 implemented according to the embodiment in this embodiment will be described in detail below with reference to FIG. 5 .
  • the electronic device 600 shown in FIG. 5 is only an example and should not impose any restrictions on the functions and scope of use of any embodiment of the technical solution of the present application.
  • electronic device 600 is embodied in the form of a general computing device.
  • the components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
  • the storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 performs the implementation steps according to this embodiment described in the above image splicing method area in this embodiment.
  • the processing unit 610 may perform the steps shown in FIGS. 1 to 4 .
  • the storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access unit (RAM) 6201 and/or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.
  • RAM random access unit
  • ROM read-only storage unit
  • Storage unit 620 may also include a program/utility 6204 having a set of (at least one) program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples, or some combination, may include the implementation of a network environment.
  • program/utility 6204 having a set of (at least one) program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples, or some combination, may include the implementation of a network environment.
  • Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures. .
  • Electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with electronic device 600, and/or with The electronic device can communicate with any device that communicates with one or more other computing devices (eg, router, modem, etc.). This communication may occur through input/output (I/O) interface 650.
  • the electronic device 600 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 660.
  • Network adapter 660 may communicate with other modules of electronic device 600 via bus 630.
  • a computer-readable storage medium is also provided.
  • a computer program is stored on the computer-readable storage medium.
  • the computer program When executed by a processor, it can realize the application in integrated circuits provided in the above embodiments. Relevant steps of the dynamic selection method of the device's data area.
  • various aspects described in the technical solution of this application can also be implemented in the form of a program product, which includes program code.
  • the program product When run on a terminal device, the program code is used to cause the terminal device to perform the steps described in the image stitching method area of the technical solution of the present application according to the implementation methods in various embodiments of the technical solution of the present application.
  • Figure 6 shows a schematic structural diagram of a computer-readable storage medium according to some embodiments of the present application.
  • a program product 800 for implementing the above method in an embodiment according to the technical solution of the present application is described. It can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be Run on terminal devices such as personal computers.
  • CD-ROM portable compact disk read-only memory
  • the program product generated according to this embodiment is not limited to this.
  • the readable storage medium can be any tangible medium that contains or stores a program.
  • the program can be used by or in conjunction with an instruction execution system, device or device. In conjunction with.
  • the Program Product may take the form of one or more readable media in any combination.
  • the readable medium may be a readable signal medium or a readable storage medium.
  • the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may include a data signal propagated in baseband or as a carrier wave having readable program code thereon. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • a readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code contained on a readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.
  • the program code for performing the operations of the technical solution of the present application can be written in any combination of one or more programming languages.
  • Programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural formulas.
  • Programming language - such as C or similar programming language.
  • the program code may execute entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, execute entirely on the user's computing device, partially on a remote computing device, or entirely on the remote computing device or server execute on.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network or a wide area network, or may be connected to an external computing device (e.g., via an Internet service provider). .

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Abstract

本发明提供了一种应用于集成电路器件的数据区域动态选取方法、系统、设备和计算机可读存储介质。通过本申请提出的技术方案，能够实现针对数据区域的动态化选取，去除了在数据区域选取过程中对于原始数据集的严格依赖，简化了针对不同集成电路器件进行数据区域选取的复杂度，提升了数据区域选取的通用性和复用性，适用范围广泛，具有可推广价值。

Description

应用于集成电路器件的数据区域动态选取方法、系统、设备和计算机可读存储介质 技术领域
本发明涉及集成电路测试技术领域，具体地，公开了一种应用于集成电路器件的数据区域动态选取方法、系统、设备和计算机可读存储介质。
背景技术
随着科学技术水平的不断发展，在集成电路领域通过建立模型对各个器件的工作进行分析已然成为了较为通用的技术手段之一，而对器件数据所反映出的行为区域通过分析进行准确划分和选取则是在集成电路器件模型参数提取的流程策略中较为关键的一环。
现有技术中，由于集成电路器件之间的差异性，包括集成电路器件的测量条件、偏置条件、数据扫描范围、单次扫描步长等因素间存在差异，导致在模型参数提取流程中难以实现自动化和通用化。
发明内容
为了解决现有技术中存在的上述问题，本发明提供一种应用于集成电路器件的数据区域动态选取方法、系统、设备及计算机可读存储介质。
在本申请的第一方面提供了一种应用于集成电路器件的数据区域动态选取方法，具体可以包括：
获取集成电路器件对应的测试数据集和/或仿真数据集；
根据用户指令，获取若干关联于集成电路器件的偏置条件变量，偏置条件变量关联于集成电路器件的待测试数据项和/或待仿真数据项；
根据用户指令，获取偏置条件变量对应的偏置条件；
根据偏置条件变量和偏置条件，生成关联于待测试数据项和/或待仿真数据项的自定义选取模型；
根据自定义选取模型，于测试数据集中选取对应的动态测试数据集；和/或
根据自定义选取模型，于仿真数据集中选取对应的动态仿真数据集；
针对集成电路器件，根据动态测试数据集执行测试操作和/或根据动态仿真数据集 执行仿真操作。
在上述第一方面的一种可能的实现中，偏置条件变量包括待测数据项的若干第一特征点；和/或
待仿真数据项的若干第二特征点。
在上述第一方面的一种可能的实现中，在待测数据项包括集成电路中关联于集成电路器件的若干第一测试点的电压的情况下，第一特征点可以包括：
第一测试点的最大电压值、第一测试点的最小电压值、第一测试点的零电压值、第一测试点的饱和电压值以及第一测试点的阀电压值。
在上述第一方面的一种可能的实现中，在待仿真据项包括集成电路中关联于集成电路器件的若干第二测试点的电压的情况下，第二特征点可以包括：
第二测试点的最大电压值、第二测试点的最小电压值、第二测试点的零电压值、第二测试点的饱和电压值以及第二测试点的阀电压值。
在上述第一方面的一种可能的实现中，偏置条件变量对应的偏置条件根据选定的若干第一特征点和/或若干第二特征点确定；
偏置条件包括由若干选定的第一特征点和/或第二特征点形成的数据区间。
在上述第一方面的一种可能的实现中，用户指令包括人工指令和机器指令；
数据区域动态选取方法还包括：
对待测试数据项和/或待仿真数据项对应的偏置条件变量进行存储；
在用户指令包括至少一项机器指令的情况下，获取待测试数据项和/或待仿真数据项对应的历史偏置条件变量；
根据历史偏置条件变量，通过机器学习获取机器指令对应的至少一项偏置条件变量。
在上述第一方面的一种可能的实现中，动态测试数据集根据测试数据集的变化而动态变化；和/或
动态仿真数据集根据仿真数据集的变化而动态变化。
本申请的第二方面提供了一种应用于集成电路器件的数据区域动态选取系统，应用于前述第一方面提供的应用于集成电路器件的数据区域动态选取方法中，包括：
获取单元，用于获取集成电路器件对应的测试数据集和/或仿真数据集；
第一设定单元，用于根据用户指令，获取若干关联于集成电路器件的偏置条件变量，偏置条件变量关联于集成电路器件的待测试数据项和/或待仿真数据项；
第二设定单元，用于根据用户指令，获取偏置条件变量对应的偏置条件；
生成单元，用于根据偏置条件变量和偏置条件，生成关联于待测试数据项和/或待仿真数据项的自定义选取模型；
选取单元，用于根据自定义选取模型，于测试数据集中选取对应的动态测试数据集；和/或
根据自定义选取模型，于仿真数据集中选取对应的动态仿真数据集；
处理单元，用于针对集成电路器件，根据动态测试数据集执行测试操作和/或根据动态仿真数据集执行仿真操作。
本申请的第三方面提供了一种应用于集成电路器件的数据区域动态选取设备，包括：
存储器，用于存储计算机程序；
处理器，用于执行计算机程序时实现前述第一方面所提供的应用于集成电路器件的数据区域动态选取方法。
本申请的第四方面提供了一种计算机可读存储介质，该种计算机可读存储介质上存储有计算机程序，在计算机程序被处理器执行时实现前述第一方面所提供的应用于集成电路器件的数据区域动态选取方法。
与现有技术相比，本申请具有如下的有益效果：
通过本申请提出的技术方案，能够实现针对数据区域的动态化选取，去除了在数据区域选取过程中对于原始数据集的严格依赖，简化了针对不同集成电路器件进行数据区域选取的复杂度，提升了数据区域选取的通用性和复用性，适用范围广泛，具有可推广价值。
附图说明
通过阅读参照以下附图对非限制性实施例所作的详细描述，本发明的其它特征、目的和优点将会变得更明显：
图1根据本申请实施例，示出了一种应用于集成电路器件的数据区域动态选取方法的流程示意图；
图2根据本申请实施例，示出了一种对偏置条件变量进行选择的部分用户界面示意图；
图3根据本申请实施例，示出了一种基于机器指令的数据区域动态选取方法的流程示意图；
图4根据本申请实施例，示出了一种应用于集成电路器件的数据区域动态选取系统的结构示意图；
图5根据本申请实施例，示出了一种应用于集成电路器件的数据区域动态选取设备的结构示意图；
图6根据本申请实施例，示出了一种计算机可读存储介质的结构示意图。
具体实施方式
下面结合具体实施例对本发明进行详细说明。以下实施例将有助于本领域的技术人员进一步理解本发明，但不以任何形式限制本发明。应当指出的是，对本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变化和改进。这些都属于本发明的保护范围。
在本文中使用的术语“包括”及其变形表示开放性包括，即“包括但不限于”。除非特别申明，术语“或”表示“和/或”。术语“基于”表示“至少区域地基于”。术语“一个示例实施例”和“一个实施例”表示“至少一个示例实施例”。术语“另一实施例”表示“至少一个另外的实施例”。术语“第一”、“第二”等等可以指代不同的或相同的对象。下文还可能包括其他明确的和隐含的定义。
针对现有技术中存在的在模型参数提取流程中难以实现自动化和通用化的问题，本申请提供了一种应用于集成电路器件的数据区域动态选取方法、系统、设备及计算机可读存储介质。通过本申请提供的技术方案，能够去除在数据区域选取过程中对于原始数据集的严格依赖，简化了针对不同集成电路器件进行数据区域选取的复杂度，提升了数据区域选取的通用性和复用性。以下将结合实施例对本申请提供的技术方案进行阐释和说明。
在本申请的一些实施例中，图1示出了一种应用于集成电路器件的数据区域动态选取方法的流程示意图。
如图1所示，该种应用于集成电路器件的数据区域动态选取方法具体可以包括：
步骤101：获取集成电路器件对应的测试数据集和/或仿真数据集。
步骤102：根据用户指令，获取若干关联于集成电路器件的偏置条件变量。其中，偏置条件变量关联于集成电路器件的待测试数据项和/或待仿真数据项。
步骤103：根据用户指令，获取偏置条件变量对应的偏置条件。其中，上述步骤102和步骤103在实际应用过程中可以同步执行，例如可以为用户提供一个选取条件的设置界面，设置界面中可以包括多个偏置条件变量的下拉菜单选项，用户可以根据需 要自行输入所需选择偏置条件变量及相关偏置条件。
步骤104：根据偏置条件变量和偏置条件，生成关联于待测试数据项和/或待仿真数据项的自定义选取模型。
步骤105：根据自定义选取模型，于测试数据集中选取对应的动态测试数据集；和/或根据自定义选取模型，于仿真数据集中选取对应的动态仿真数据集。
步骤106：针对集成电路器件，根据动态测试数据集执行测试操作和/或根据动态仿真数据集执行仿真操作。
可以理解的是，通过上述步骤101至步骤106，能够实现对于数据区域的动态选取。以下将对于上述步骤101至步骤106的具体实现做出进一步阐释和说明。
在本申请的一些实施例中，偏置条件变量包括待测数据项的若干第一特征点；和/或待仿真数据项的若干第二特征点。
以下将以具体实施例对“锚点”第一特征点和第二特征点的选取进行具体的说明和阐释：在本申请的一些具体实施例中，可以将待测试数据项或是待仿真数据项设置为电压参数，那么在待测数据项包括集成电路中关联于集成电路器件的若干第一测试点的电压的情况下，第一特征点可以包括第一测试点的最大电压值、第一测试点的最小电压值、第一测试点的零电压值、第一测试点的饱和电压值以及第一测试点的阀电压值。在待仿真据项包括集成电路中关联于集成电路器件的若干第二测试点的电压的情况下，第二特征点可以包括：第二测试点的最大电压值、第二测试点的最小电压值、第二测试点的零电压值、第二测试点的饱和电压值以及第二测试点的阀电压值。
具体地，图2示出了一种对偏置条件变量进行选择的用户界面示意图。可以看出，当对于某一集成电路器件的电压参数进行偏置条件变量进行设定的过程中，可以分别选择Vg Reference、Vd Reference以及Vb Reference作为“锚点”。如图2所示，在该用户界面中可以通过下拉菜单的方式对于上述“锚点”的具体变量信息进行勾选。
进一步地，于上述实施例中，偏置条件变量对应的偏置条件可以根据选定的若干第一特征点和/或若干第二特征点确定。例如，偏置条件可以包括由若干选定的第一特征点和/或第二特征点形成的数据区间。
在本申请的一些实施例中，可以理解的是，用户指令既可以包括人工指令和机器指令，在用户指令包含机器指令的情况下，说明本申请提供的数据区域动态选取方法能够适用于自动或半自动模型参数提取流程策略。
具体地，图3示出了一种基于机器指令的数据区域动态选取方法的流程示意图， 具体如图3所示可以包括：
步骤301：对待测试数据项和/或待仿真数据项对应的偏置条件变量进行存储。
步骤302：在用户指令包括至少一项机器指令的情况下，获取待测试数据项和/或待仿真数据项对应的历史偏置条件变量。
步骤303：根据历史偏置条件变量，通过机器学习获取机器指令对应的至少一项偏置条件变量。
可以理解的是，在执行自动或半自动模型参数提取流程策略的过程中，可以基于待测试数据项和/或待仿真数据项，根据数据自身特征以及历史偏置条件变量通过机器学习等方式对于该数据项的当下偏置条件变量进行确认。
在本申请的一些实施例中，动态测试数据集可以根据测试数据集的变化而动态变化；和/或动态仿真数据集可以根据仿真数据集的变化而动态变化。
在本申请的一些实施例中，上述应用于集成电路器件的数据区域动态选取方法可以应用于当前BSIMProplus TM、SDEP产品中。其中BSIMProplus TM是业界最领先的半导体器件SPICE建模平台，在其产品二十多年的历史中一直为全球SPICE建模市场和技术的领导者，被全球一百多家领先的集成电路制造和设计公司作为标准SPICE建模工具所采用。基于其集成的并行SPICE引擎，BSIMProplus TM提供业界最为强大的全集成SPICE建模平台，可以用于对各种半导体器件从低频到高频的各种器件特性的SPICE建模，包括电学特性测试、器件模型自动参数提取和优化、模型验证等。本申请提供的技术方案能够实现与上述现有平台的无缝对接。
在本申请的一些实施例中，图4示出了一种应用于集成电路器件的数据区域动态选取系统，应用于前述实施例提供的应用于集成电路器件的数据区域动态选取方法中，具体可以包括：
获取单元001，用于获取集成电路器件对应的测试数据集和/或仿真数据集。
第一设定单元002，用于根据用户指令，获取若干关联于集成电路器件的偏置条件变量，偏置条件变量关联于集成电路器件的待测试数据项和/或待仿真数据项。
第二设定单元003，用于根据用户指令，获取偏置条件变量对应的偏置条件。
生成单元004，用于根据偏置条件变量和偏置条件，生成关联于待测试数据项和/或待仿真数据项的自定义选取模型。
选取单元005，用于根据自定义选取模型，于测试数据集中选取对应的动态测试数据集；和/或根据自定义选取模型，于仿真数据集中选取对应的动态仿真数据集。
处理单元006，用于针对集成电路器件，根据动态测试数据集执行测试操作和/或根据动态仿真数据集执行仿真操作。
可以理解的是，于上述实施例中，获取单元001至处理单元006所执行的功能与前述实施例中步骤101至步骤106所执行的动作相一致，在此不做赘述。
在本申请的一些实施例中，还提供了一种应用于集成电路器件的数据区域动态选取设备，该种设备可以包括：
存储器，用于存储计算机程序；
处理器，用于执行计算机程序时实现本申请技术方案中说明的图像拉正方法的步骤。
可以理解的是，本申请技术方案的各个方面可以实现为系统、方法或程序产品。因此，本申请技术方案的各个方面可以具体实现为以下形式，即完全的硬件实施方式、完全的软件实施方式(包括固件、微代码等)，或硬件和软件方面结合的实施方式，这里可以统称为“电路”、“模块”或“平台”。
图5根据本申请的一些实施例，示出了一种应用于集成电路器件的数据区域动态选取设备的结构示意图。下面参照图5来详细描述根据本实施例中的实施方式实施的电子设备600。图5显示的电子设备600仅仅是一个示例，不应对本申请技术方案任何实施例的功能和使用范围带来任何限制。
如图5所示，电子设备600以通用计算设备的形式表现。电子设备600的组建可以包括但不限于：至少一个处理单元610、至少一个存储单元620、连接不同平台组件(包括存储单元620和处理单元610)的总线630、显示单元640等。
其中，存储单元存储有程序代码，程序代码可以被处理单元610执行，使得处理单元610执行本实施例中上述图像拼接方法区域中描述的根据本实施例中的实施步骤。例如，处理单元610可以执行如图1至图4中所示的步骤。
存储单元620可以包括易失性存储单元形式的可读介质，例如随机存取单元(RAM)6201和/或高速缓存存储单元6202，可以进一步包括只读存储单元(ROM)6203。
存储单元620还可以包括具有一组(至少一个)程序模块6205的程序/实用工具6204，这样的程序模块6205包括但不限于：操作系统、一个或多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。
总线630可以表示几类总线结构中的一种或多种，包括存储单元总线或者存储单元控制器、外围总线、图像加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。
电子设备600也可以与一个或多个外部设备700(例如键盘、指向设备、蓝牙设备等)通信，还可以与一个或者多个使得用户与该电子设备600交互的设备通信，和/或与使得该电子设备能与一个或多个其他计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口650进行。并且，电子设备600还可以通过网络适配器660与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。网络适配器660可以通过总线630与电子设备600的其他模块通信。应当明白，尽管图5中未示出，可以结合电子设备600使用其他硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储平台等。
在本申请的一些实施例中，还提供了一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时能够实现上述实施例中提供的应用于集成电路器件的数据区域动态选取方法的相关步骤。
尽管本实施例未详尽地列举其他具体的实施方式，但在一些可能的实施方式中，本申请技术方案说明的各个方面还可以实现为一种程序产品的形式，其包括程序代码，当程序产品在终端设备上运行时，程序代码用于使终端设备执行本申请技术方案中图像拼接方法区域中描述的根据本申请技术方案各种实施例中实施方式的步骤。
图6根据本申请的一些实施例示出了一种计算机可读存储介质的结构示意图。如图6所示，其中描述了根据本申请技术方案的实施方式中用于实现上述方法的程序产品800，其可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码，并可以在终端设备，例如个人电脑上运行。当然，依据本实施例产生的程序产品不限于此，在本申请技术方案中，可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
计算机可读存储介质可以包括在基带中或者作为载波一区域传播的数据信号，其中 承载了可读程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。可读存储介质还可以是可读存储介质以外的任何可读介质，该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。可读存储介质上包含的程序代码可以用任何适当的介质传输，包括但不限于无线、有线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言的任意组合来编写用于执行本申请技术方案操作的程序代码，程序设计语言包括面向对象的程序设计语言—诸如Java、C++等，还包括常规的过程式程序设计语言—诸如C语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、区域地在用户设备上执行、作为一个独立的软件包执行、区域在用户计算设备上区域在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括局域网或广域网，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。
综上所述，通过本申请提出的技术方案，能够实现针对数据区域的动态化选取，去除了在数据区域选取过程中对于原始数据集的严格依赖，简化了针对不同集成电路器件进行数据区域选取的复杂度，提升了数据区域选取的通用性和复用性，适用范围广泛，具有可推广价值。
上述描述仅是对本申请技术方案较佳实施例的描述，并非对本申请技术方案范围的任何限定，本申请技术方案领域的普通技术人员根据上述揭示内容做的任何变更、修饰，均属于权利要求书的保护范围。

Claims (10)

  1. 一种应用于集成电路器件的数据区域动态选取方法，其特征在于，所述数据区域动态选取方法包括：
    获取所述集成电路器件对应的测试数据集和/或仿真数据集；
    根据用户指令，获取若干关联于所述集成电路器件的偏置条件变量，所述偏置条件变量关联于所述集成电路器件的待测试数据项和/或待仿真数据项；
    根据所述用户指令，获取所述偏置条件变量对应的偏置条件；
    根据所述偏置条件变量和所述偏置条件，生成关联于所述待测试数据项和/或所述待仿真数据项的自定义选取模型；
    根据所述自定义选取模型，于所述测试数据集中选取对应的动态测试数据集；和/或
    根据所述自定义选取模型，于所述仿真数据集中选取对应的动态仿真数据集；
    针对所述集成电路器件，根据所述动态测试数据集执行测试操作和/或根据所述动态仿真数据集执行仿真操作。
  2. 如权利要求1所述的应用于集成电路器件的数据区域动态选取方法，其特征在于，所述偏置条件变量包括所述待测数据项的若干第一特征点；和/或
    所述待仿真数据项的若干第二特征点。
  3. 如权利要求2所述的应用于集成电路器件的数据区域动态选取方法，其特征在于，在所述待测数据项包括集成电路中关联于所述集成电路器件的若干第一测试点的电压的情况下，所述第一特征点可以包括：
    所述第一测试点的最大电压值、所述第一测试点的最小电压值、所述第一测试点的零电压值、所述第一测试点的饱和电压值以及所述第一测试点的阀电压值。
  4. 如权利要求2所述的应用于集成电路器件的数据区域动态选取方法，其特征在于，在所述待仿真据项包括集成电路中关联于所述集成电路器件的若干第二测试点的电压的情况下，所述第二特征点可以包括：
    所述第二测试点的最大电压值、所述第二测试点的最小电压值、所述第二测试点的零电压值、所述第二测试点的饱和电压值以及所述第二测试点的阀电压值。
  5. 如权利要求2所述的应用于集成电路器件的数据区域动态选取方法，其特征在于，所述偏置条件变量对应的所述偏置条件根据选定的若干所述第一特征点和/或若干所述第二特征点确定；
    所述偏置条件包括由若干选定的所述第一特征点和/或所述第二特征点形成的数据区间。
  6. 如权利要求1所述的应用于集成电路器件的数据区域动态选取方法，其特征在于，所述用户指令包括人工指令和机器指令；
    所述数据区域动态选取方法还包括：
    对所述待测试数据项和/或所述待仿真数据项对应的所述偏置条件变量进行存储；
    在所述用户指令包括至少一项所述机器指令的情况下，获取所述待测试数据项和/或所述待仿真数据项对应的历史偏置条件变量；
    根据所述历史偏置条件变量，通过机器学习获取所述机器指令对应的至少一项所述偏置条件变量。
  7. 如权利要求1所述的应用于集成电路器件的数据区域动态选取方法，其特征在于，所述动态测试数据集根据所述测试数据集的变化而动态变化；和/或
    所述动态仿真数据集根据所述仿真数据集的变化而动态变化。
  8. 一种应用于集成电路器件的数据区域动态选取系统，其特征在于，应用于如权利要求1至7中任意一项所述的应用于集成电路器件的数据区域动态选取方法中，包括：
    获取单元，用于获取所述集成电路器件对应的测试数据集和/或仿真数据集；
    第一设定单元，用于根据用户指令，获取若干关联于所述集成电路器件的偏置条件变量，所述偏置条件变量关联于所述集成电路器件的待测试数据项和/或待仿真数据项；
    第二设定单元，用于根据所述用户指令，获取所述偏置条件变量对应的偏置条件；
    生成单元，用于根据所述偏置条件变量和所述偏置条件，生成关联于所述待测试数据项和/或所述待仿真数据项的自定义选取模型；
    选取单元，用于根据所述自定义选取模型，于所述测试数据集中选取对应的动态测试数据集；和/或
    根据所述自定义选取模型，于所述仿真数据集中选取对应的动态仿真数据集；
    处理单元，用于针对所述集成电路器件，根据所述动态测试数据集执行测试操作和/或根据所述动态仿真数据集执行仿真操作。
  9. 一种应用于集成电路器件的数据区域动态选取设备，其特征在于，包括：
    存储器，用于存储计算机程序；
    处理器，用于执行所述计算机程序时实现如权利要求1至7中任一项所述的应用于集成电路器件的数据区域动态选取方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至7中任一项所述的应用于集成电路器件的数据区域动态选取方法。
PCT/CN2022/142570 2022-05-06 2022-12-28 应用于集成电路器件的数据区域动态选取方法、系统、设备和计算机可读存储介质 WO2023213094A1 (zh)

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