Dynamic Data Region Selection for Integrated Circuit Testing
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Solution Overview
Problem
Existing integrated circuit testing methods face challenges in achieving automation and generalization due to differences in measurement conditions, bias conditions, and data scanning ranges, making data region selection complex and non-universal.
Innovation Solution
A dynamic selection method and system that involves acquiring bias condition variables, generating a customized selection model, and selecting dynamic test or simulation datasets based on user instructions, including manual and machine learning, to simplify and enhance the universality of data region selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional fixed data region selection methods are used, then the process is simple to implement, but it cannot achieve automation and generalization due to differences in measurement conditions, bias conditions, and data scanning ranges
Solution Approach 1:
The patent implements dynamic data region selection by allowing the selection model to automatically adjust data regions based on different measurement conditions, bias conditions, and scanning ranges. The system dynamically determines optimal data regions rather than using fixed predefined regions, enabling adaptation to varying test conditions while maintaining automation.
Solution Approach 2:
The patent changes key parameters including measurement conditions, bias conditions, and scanning ranges as inputs to the selection model. By varying these parameters dynamically based on the specific integrated circuit device being tested, the system achieves generalization across different devices while maintaining automated operation.
2Adaptability or versatility
If customized selection models are generated for each device, then universality and reusability are improved, but the initial setup and model generation complexity increases
Solution Approach 1:
The patent creates a universal selection model that can be applied across different integrated circuit devices. The model is designed to handle various measurement conditions, bias conditions, and device types, making it reusable and adaptable. Once established, the same model framework serves multiple devices and test scenarios, achieving universality.
Solution Approach 2:
The patent performs preliminary model generation and validation before actual device testing. By pre-establishing the selection model with appropriate parameters and validation rules, the system reduces complexity during actual use. The model is prepared in advance to handle different scenarios, making the deployment phase simpler.
Data Source
AI summary
The present application provides a dynamic selection method, system and device for a data region applied to an integrated circuit device, and a computer-readable storage medium. Through the technical solutions provided by the present application, the dynamic selection for the data region can be achieved, the strict dependence on an original dataset in the data region selection process is removed, the complexity of data region selection for different integrated circuit devices is simplified, and the universality and reusability of data region selection are improved. The application range is wide, and the popularization value is achieved.