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context-aware-dfm-rule-analysis-and-scoring-using-machine-learning.pdf

🔗 Source: dvcon-proceedings.org

📊 Size: 958 KB

👤 Author: TRIPATHI, VIKAS

⬇️ Downloads: 130

Summary

To evaluate physical layout designs' manufacturability, DFM rule scoring techniques are used, but conventional methodologies have limitations, as they don't consider neighboring geometric scenarios, leading to inaccurate scores. A novel approach using machine learning analyzes the context of targeted layouts and predicts their lithography impacts on manufacturability. The proposed methodology uses artificial neural networks trained with supervised machine learning techniques and labeled data from past lithography simulation results to predict problematic layout contexts and their probability of causing failures. The training process involves data extraction, overlaying DFM violations with lithography hotspots, and classifying them as "bad" or "good" contexts. Layout context is translated into metric form and used as input for the neural network, which computes weights and biasing for different synapses to predict lithography impacts.

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Description

To evaluate physical layout designs' manufacturability, DFM rule scoring techniques are used, but conventional methodologies have limitations, as they don't...

Technical Information

File Format: PDF
File Size: 958 KB
Pages: 9
Language: EN
Author: TRIPATHI, VIKAS
Total Downloads: 130
Last Updated: 1 month ago

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