ML-Based EDA Layout Design Optimization
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Solution Overview
Problem
The miniaturization of integrated circuits poses challenges in design and manufacturing, requiring stricter specifications and reliability, which existing electronic design automation (EDA) tools struggle to address effectively, especially in optimizing layout designs for varying technology nodes and transistor types.
Innovation Solution
The use of machine learning techniques, such as Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), and Reinforcement Learning (RL), to optimize layout design flows, including operations like logic synthesis, placement, and routing, by comparing initial user design content with reference design content to determine similarity and adjust design recipes iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing EDA tools are used for layout design optimization, then the design process can be completed, but the quality of results deteriorates due to inability to meet stringent specifications at miniaturized technology nodes
Solution Approach 1:
The patent replaces traditional rule-based EDA tools with machine learning models (CNN, GCN, RL) that have learned optimal design patterns from training data. This substitution enables the system to handle complex miniaturization constraints that traditional mechanical/rules-based approaches cannot resolve, achieving both high precision and reliability at advanced technology nodes
Solution Approach 2:
The system dynamically adjusts design parameters and optimization objectives based on the specific technology node and transistor type being designed. By changing parameters such as cell size, spacing, and layout configuration according to the target technology node's capabilities, the system achieves optimal results across different miniaturization levels
2Adaptability or versatility
If machine learning models are trained on diverse design data, then the adaptability to different technology nodes improves, but the system complexity increases
Solution Approach 1:
The patent segments the machine learning system into separate models or modules, each specialized for specific technology nodes or transistor types. This segmentation allows the system to maintain high adaptability across different nodes while managing complexity by only loading and activating relevant models for each specific design task
Solution Approach 2:
The system uses pre-trained machine learning models that have been copied and adapted for different technology nodes. Instead of training new models from scratch for each node, the system leverages pre-existing trained models that can be fine-tuned or selected based on the target technology node, reducing overall system complexity while maintaining versatility
3Manufacturing precision
If iterative optimization is performed to improve layout quality, then the manufacturing precision improves, but the design time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive design data before actual layout optimization is needed. This preliminary training enables the models to quickly generate high-quality layouts without requiring time-consuming iterative optimization during the actual design process, significantly reducing design cycle time while maintaining high precision
Solution Approach 2:
The system skips traditional time-consuming iterative optimization steps by using machine learning models that can directly generate optimized layouts in a single forward pass. The models have already learned the optimal solutions from training data, allowing the system to rush through the optimization process and achieve high-quality results much faster than traditional iterative methods
Data Source
AI summary
A method includes constructing a set of reference design contents associated with a set of reference design recipes. The method also includes determining a content similarity between a user design content and a reference design content taken from the set of reference design contents. The method further includes executing a design flow specified by a reference design recipe associated with the reference design content, as a result of the content similarity reaching a predetermined threshold.