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April 30, 2026JSTS Journal of Semiconductor Technology and Science0 citations

ML-Driven Optimization of Standard Cells for Performance and Timing in Advanced Nodes

ML-Driven Optimization of Standard Cell Performance and Timing in Advanced Nodes

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Authors

HJHyunJoon JeongJSJunHa SukJKJeong-Taek Kong

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Overview

Randomized trial demonstrates performance improvement in standard cells, highlighting machine learning implications.

Key Points

  • This research aims to optimize standard cell performance and timing in advanced nodes using machine learning techniques.
  • Developed a machine learning-based methodology for standard cell optimization.
  • Performed post-layout simulations with parasitic component extraction (PEX) for delay and power calculations.
  • Trained an artificial neural network (ANN) and used multi-objective Bayesian optimization to refine standard cell designs.
  • For HP INV cells, delay reduced by up to 23.2%; for LP NAND2 cells, power reduced by 10.3%.
  • Rise-fall delay balance improved by over 15% in NAND2/NOR2 cells.
  • Significant improvements in delay and power efficiency were observed in optimized test circuits including a 7-stage ring oscillator and a 4-bit ripple carry adder.

Cite This Study

Jeong et al. (2026) studied this question.

synapsesocial.com/papers/69f2f0e31e5f7920c6386e7bhttps://doi.org/10.5573/jsts.2026.26.2.130
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