HKMG FinFET Process Variation Analysis Method
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Solution Overview
Problem
Current methods lack comprehensive analysis of the interaction between process parameters and electrical characteristics of HKMG FinFET devices, making it difficult to identify and optimize the sources of process variations affecting transistor electrical characteristics.
Innovation Solution
An analysis method that performs key process parameter correlation analysis, sensitivity analysis, and data mining to determine the relationship between electrical parameters and key process parameters, using a knowledge database to identify correlations and empirical formulas, and adjust process parameters for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive analysis of process parameters and electrical characteristics is performed, then identification accuracy of process variation sources is improved, but analysis complexity and computational resources increase
Solution Approach 1:
The patent segments the comprehensive analysis into distinct modules: data collection from multiple process steps, electrical parameter measurement, correlation analysis between process and electrical parameters, and optimization recommendation. This segmentation allows each module to be processed independently, reducing overall analysis complexity while maintaining comprehensive coverage of process- electrical characteristic relationships.
Solution Approach 2:
The patent transforms the analysis approach by changing parameters from traditional single-step analysis to multi-parameter correlation analysis. It introduces correlation coefficients and sensitivity analysis parameters to quantify relationships between process parameters and electrical characteristics, enabling automated identification of key process variation sources without manually increasing analysis complexity.
2Reliability
If interaction effects of all process steps are integrated into comprehensive analysis, then optimization effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary correlation analysis to identify key process parameters that have significant influence on electrical characteristics before conducting full optimization analysis. By pre-screening process steps and parameters based on correlation coefficients, the system reduces the scope of subsequent comprehensive analysis, maintaining optimization effectiveness while reducing processing time.
Solution Approach 2:
The patent implements feedback mechanisms where analysis results from previous process steps inform subsequent analysis. The system uses measured electrical parameters to feedback into process parameter optimization, creating an iterative refinement process that converges faster than exhaustive analysis while achieving comparable optimization effectiveness.
3Measurement precision
If sensitivity analysis is performed on each electrical-process model, then key process parameter identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial sensitivity analysis by focusing computational resources on the most influential process parameters identified through preliminary correlation analysis. Instead of performing exhaustive sensitivity analysis on all process parameters, the system selectively analyzes only those parameters that show significant correlation with electrical characteristics, achieving high identification accuracy with reduced computational complexity.
Data Source
AI summary
The present invention provides an analysis method for a semiconductor device for analyzing a plurality of electrical parameters of a HKMG fin field effect transistor and a plurality of process parameters for manufacturing the transistor, comprising: performing key process parameter correlation analysis for each electrical parameter, wherein the key process parameter correlation analysis comprises: constructing multiple electrical-process models of the electrical parameter corresponding to each process parameter respectively; performing sensitivity analysis for each of the electrical-process models; determining a plurality of key process parameters from the plurality of process parameters based on the obtained sensitivity analysis results of the electrical-process models; and determining a relationship between the electrical parameter and the plurality of key process parameters based on a knowledge database. According to the analysis method provided by the present invention, the process variation which truly causes the transistor's electrical characteristics to change is excavated through the expert system.