Semiconductor Circuit Migration Prediction With Transfer Learning
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Solution Overview
Problem
Existing semiconductor circuit design processes face challenges in efficiently migrating to new technology nodes without the need for extensive physical testing, as they lack effective methods for predicting performance characteristics accurately and efficiently.
Innovation Solution
A machine learning framework utilizing transfer learning and deep learning to train a neural network with data from known semiconductor circuits, enabling prediction of performance characteristics in new technology nodes based on old technology data, with optional small amounts of new technology data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional physical testing methods are used for semiconductor circuit migration to new technology nodes, then measurement precision and reliability are improved, but loss of time and productivity deteriorate due to time-consuming physical tests and resource-intensive design iterations
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance using historical semiconductor circuit data from known technology nodes. This pre-trained model can then predict performance characteristics for new technology nodes without requiring physical prototypes or extensive testing, thereby reducing design iteration time while maintaining prediction accuracy.
Solution Approach 2:
The patent uses copying by creating virtual representations of semiconductor circuits through machine learning models. Instead of physically testing actual circuits, the system creates digital twins or simulated circuit behaviors based on training data, allowing performance prediction without physical prototypes and significantly reducing testing time and resource requirements.
2Productivity
If machine learning models are trained only on old technology data, then device complexity and resource requirements are reduced, but measurement precision and adaptability deteriorate due to limited accuracy for new technology nodes
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive historical data from multiple known technology nodes before deployment. This preliminary training phase equips the model with fundamental circuit behavior patterns that can be adapted to new technology nodes, improving prediction accuracy while maintaining design efficiency.
Solution Approach 2:
The patent uses parameter changes by adjusting model parameters and incorporating technology node-specific features when predicting performance for new nodes. The system dynamically adapts the pre-trained model by modifying parameters based on the target technology node characteristics, thereby maintaining high prediction accuracy across different technology generations without requiring complete retraining.
Data Source
AI summary
A method, computer system, and a computer program product are provided. A trained machine learning model is used to predict performance of a semiconductor circuit. The using includes inputting, to the trained machine learning model, semiconductor physical characteristics of the semiconductor circuit and in response receiving, as output from the trained machine learning model, predicted performance characteristics for the semiconductor circuit. The trained machine learning model was trained on training data obtained from a prior known semiconductor circuit.