Standard Cell Design Optimization via ML Parameter Adjustment
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Solution Overview
Problem
Current standard cell design processes for semiconductor integrated circuits face challenges in optimizing parameters such as performance, power consumption, area, and yield, especially with the advancement to sub-micron technologies, where existing methods are inefficient and time-consuming.
Innovation Solution
A standard cell design system that includes a control engine for determining planar and vertical parameters, a 3D structure generating engine, an extraction engine, an assessment engine, and an auto-optimizing engine using machine learning algorithms to adjust parameters based on assessment results, optimizing the design of standard cells to meet reference values for performance, power, area, and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional standard cell design processes are used, then design completeness is maintained, but optimization time is excessive and efficiency is low
Solution Approach 1:
The system employs an auto-optimizing engine that automatically adjusts planar and vertical parameters of standard cells based on machine learning algorithms and assessment results, eliminating the need for manual optimization and significantly reducing design time while maintaining high efficiency
Solution Approach 2:
The system implements a closed-loop optimization process where the assessment engine evaluates standard cell performance metrics and feeds results back to the auto-optimizing engine, which then iteratively adjusts parameters to achieve optimal design outcomes
2Reliability
If existing optimization methods are applied, then some parameters are improved, but overall design quality and yield remain insufficient
Solution Approach 1:
The assessment engine performs multiple assessment operations simultaneously on various standard cell parameters including performance, power consumption, area, and yield, providing comprehensive design quality evaluation that improves overall reliability while maintaining efficiency through automated multi-criteria optimization
Data Source
AI summary
A standard cell design system is provided. The standard cell design system includes at least one processor configured to implement: a control engine that determines planar parameters and vertical parameters of a target standard cell, a three-dimensional structure generating engine that generates a three-dimensional structure of the target standard cell based on the planar parameters and the vertical parameters, an extraction engine that extracts a standard cell model of the target standard cell from the three-dimensional structure, an assessment engine that performs a plurality of assessment operations based on the standard cell model, and an auto-optimizing engine that adjusts, based on a machine learning algorithm, the planar parameters and the vertical parameters based on results of the plurality of assessment operations.