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Machine Learning Prediction for Design and System Technology Co-Optimization Sensitivity Analysis by Chung-Kuan Cheng; Chia-Tung Ho; Chester Holtz; Daeyeal Lee; Bill Lin is a Engineering article available to read on EtoBox.

What is Machine Learning Prediction for Design and System Technology Co-Optimization Sensitivity Analysis about?

As technology nodes continue to advance relentlessly, geometric pitch scaling starts to slow down. In order to retain the trend of Moore's law, design technology co-optimization (DTCO) and system technology co-optimization (STCO) are introduced together to continue scaling beyond 5 nm using pitch scaling, patterning, and novel 3-D cell structures [i.e., complementary-FET (CFET)]. However, numerous DTCO and STCO iterations are needed to continue block-level area scaling with considerations of physical layout factors: 1) various standard cell (SDC) library sets (i.e., different cell heights and conventional FET); 2) design rules (DRs); 3) back end of line (BEOL) settings; and 4) power delivery network (PDN) configurations. The growing turnaround time (TAT) among SDC design, DR optimization, and block-level area evaluation becomes one of the major bottlenecks in DTCO and STCO explorations. In this work, we develop a machine learning model that combines bootstrap aggregation and gradient boosting techniques to predict the sensitivity of minimum valid block-level area of various physical layout factors. We first demonstrate that the proposed model achieves 16.3% less mean absolute error

Who reads Machine Learning Prediction for Design and System Technology Co-Optimization Sensitivity Analysis?

Author

Chung-Kuan Cheng; Chia-Tung Ho; Chester Holtz; Daeyeal Lee; Bill Lin

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)