IC Model Parameter Extraction With Automated Data Rule Checking
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Solution Overview
Problem
The existing methods for extracting model parameters of integrated circuit devices are labor-intensive and time-consuming due to reliance on human judgment and manual operations, leading to inconsistent and inaccurate data screening during parameter extraction.
Innovation Solution
A method involving a setting interface with data checking and extraction lists, automated rule checking, and data marking to generate target data sets, allowing for accurate and automated data division and extraction of model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual operations and human judgment are used for data screening during parameter extraction, then flexibility in handling complex data can be maintained, but labor intensity increases and extraction time is consumed
Solution Approach 1:
The patent replaces manual mechanical operations with automated computer-based systems. The parameter extraction system automatically screens data, identifies valid data points, and extracts model parameters without requiring manual intervention, thereby reducing time consumption while maintaining operational effectiveness through programmable logic.
Solution Approach 2:
The system performs self-service by automatically validating data quality, checking measurement requirements, and extracting parameters independently. The automated workflow includes self-checking of data consistency, self-validation against predefined criteria, and self-extraction of model parameters, eliminating the need for manual oversight in routine operations.
2Adaptability or versatility
If manual data screening is performed based on human judgment, then adaptability to different data characteristics can be maintained, but stability and consistency of extraction results deteriorate
Solution Approach 1:
The system maintains adaptability by allowing configurable parameter settings that can be adjusted according to different data characteristics. Users can modify extraction criteria, validation thresholds, and selection parameters through interface settings, enabling the system to adapt to various data types while maintaining consistent and reliable extraction results through programmed logic rather than subjective judgment.
Solution Approach 2:
The system incorporates feedback mechanisms that automatically validate extracted parameters against predefined criteria and measurement requirements. The automated workflow includes feedback loops that check data quality, verify parameter consistency, and ensure extraction results meet established standards, thereby maintaining high reliability and consistency across multiple extraction operations.
3Productivity
If automated rule checking is implemented for data screening, then extraction efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent segments the parameter extraction system into distinct functional modules: data collection module, data validation module, parameter extraction module, and result verification module. Each module handles specific tasks independently, making the overall complex automated system manageable and maintainable through modular architecture while achieving high extraction efficiency through coordinated operation of these segments.
Data Source
AI summary
A method for extracting a model parameter of an integrated circuit device, an apparatus and a storage medium. The method includes providing a test data set and a simulated data set for an integrated circuit device; providing a setting interface including a data checking list and a data extraction list; a user setting the data checking list of a setting interface; generating at least one data checking task on the basis of a user input setting, and performing rule checking on a test data set and a simulated data set according to a pre-stored data checking package, automatically marking, and generating one new target data set; a user setting the data extraction list of the setting interface and extracting parameters of one or more newly generated target data sets; and modeling according to the parameters extracted according to the data extraction list.


