Graph Neural Timing Prediction Transfer Across Process Nodes
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Solution Overview
Problem
Adapting machine learning models from an older to a newer semiconductor technology node is challenging due to significant differences in manufacturing processes, requiring substantial modifications to accurately predict circuit behavior and performance metrics.
Innovation Solution
A transfer learning framework is introduced to adapt a graph-based deep neural regression model from a 65 nm technology node to a 28 nm technology node, leveraging pre-trained models and minimizing the need for retraining by fine-tuning with a smaller dataset, using graph convolutional neural networks (GCNs) and freezing layers to preserve knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are adapted from an older to a newer semiconductor technology node using conventional methods, then prediction accuracy for circuit behavior can be improved, but substantial modifications and retraining are required, increasing computational time and resources
Solution Approach 1:
The model is pre-trained on data from an older technology node (e.g., 65 nm) before being deployed to the newer technology node (e.g., 28 nm). This preliminary training establishes a baseline model that captures general circuit behavior patterns, which can then be efficiently adapted to the new node with minimal additional training, significantly reducing the computational time required for adaptation while maintaining prediction accuracy.
Solution Approach 2:
The invention adapts the pre-trained model to the new technology node by modifying key parameters such as transistor width, length, and threshold voltage to reflect the characteristics of the newer node. These parameter changes allow the model to accurately predict circuit behavior at the new technology node without requiring complete retraining, thus improving prediction accuracy while minimizing computational resource requirements.
2Measurement precision
If machine learning models are adapted from an older to a newer semiconductor technology node using conventional methods, then prediction accuracy for circuit behavior can be improved, but substantial modifications and retraining are required, increasing computational resources
Solution Approach 1:
The model is pre-trained on data from an older technology node (e.g., 65 nm) before being deployed to the newer technology node (e.g., 28 nm). This preliminary training establishes a baseline model that captures general circuit behavior patterns, which can then be efficiently adapted to the new node with minimal additional training, significantly reducing the computational time required for adaptation while maintaining prediction accuracy.
Solution Approach 2:
The invention adapts the pre-trained model to the new technology node by modifying key parameters such as transistor width, length, and threshold voltage to reflect the characteristics of the newer node. These parameter changes allow the model to accurately predict circuit behavior at the new technology node without requiring complete retraining, thus improving prediction accuracy while minimizing computational resource requirements.
3Measurement precision
If a model is fully retrained on a new technology node dataset, then prediction accuracy for that node is improved, but the need for substantial retraining data and computational resources increases
Solution Approach 1:
The model is pre-trained on data from an older technology node (e.g., 65 nm) before being deployed to the newer technology node (e.g., 28 nm). This preliminary training establishes a baseline model that captures general circuit behavior patterns, which can then be efficiently adapted to the new node with minimal additional training, significantly reducing the computational time required for adaptation while maintaining prediction accuracy.
Solution Approach 2:
The invention adapts the pre-trained model to the new technology node by modifying key parameters such as transistor width, length, and threshold voltage to reflect the characteristics of the newer node. These parameter changes allow the model to accurately predict circuit behavior at the new technology node without requiring complete retraining, thus improving prediction accuracy while minimizing computational resource requirements.
Data Source
AI summary
Methods, systems, and computer-readable media predict post-routing timing metrics in integrated circuit design. A graph-based neural network may be trained on timing path graphs from a first semiconductor process node and adapted to a second node through fine-tuning, layer freezing, or architecture modification. Timing path graphs may be generated from netlist, layout, parasitic, and variation data and processed by a graph-based model to predict downstream metrics from earlier design stages. Certain techniques encompass transfer learning across process nodes, multi-metric adaptation, multi-stage prediction, and automated architecture selection, and are applicable to various semiconductor technology generations.