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AI in SIGNAL INTEGRITY. A highly robust and optimised model using MLP-Regressor.
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[Audio] Topic: AI In Signal Integrity Problem Statement - To address the issue of return loss during signal transmission via the connector, a proposed meta model aims to minimize data return loss for a specific data rate, enabling suitable modifications to the connector design. ML Method and Approach: A highly robust and optimized deep learning model using MLPRegressor. Error Details: Mean Absolute Percentage Error(MAPE): 5.79%; Mean Percentage Error(MPE): 0.63%; Loss of model: 0.00356 Robustness and Pass/Fail Data: Done By, Tech Fusion A synergy of Varun Bhattacharya Ketan Sarvesh Agrawal Piyush Kumar.
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[Audio] Topic: AI In Signal Integrity Problem Statement - To address the issue of return loss during signal transmission via the connector, a proposed meta model aims to minimize data return loss for a specific data rate, enabling suitable modifications to the connector design. ML Method and Approach: A highly robust and optimized deep learning model using MLPRegressor. Error Details: Mean Absolute Percentage Error(MAPE): 5.79%; Mean Percentage Error(MPE): 0.63%; Loss of model: 0.00356 Robustness and Pass/Fail Data: Done By, Tech Fusion A synergy of Varun Bhattacharya Ketan Sarvesh Agrawal Piyush Kumar.