Work

Optimization of 6T-SRAM Cell Based on CNN-Informed NSGA-II with Consideration of Parasitic Resistance

Year: 2025

Type: article

Abstract: Optimizing static random-access memory (SRAM) cells requires considering parasitic effects, as their impact on circuits in advanced nodes becomes increasingly complex. In this paper, Convolutional Neural Network-Informed Non-dominated Sorting Genetic Algorithms-II (CNN-Informed NSGA-II) was proposed to optimize 7 nm FinFET 6T-SRAM cells taking into account parasitic resistance. CNN-Informed NSGA-II uses a trained CNN model integrated into the conventional NSGA-II, thereby reducing its computational complexity. This approach provides a generally applicable solution that significantly improves the efficiency of circuits while balancing competitive performance metrics. Compared to the ideal (parasitic-free) 6T-SRAM cell design, the optimized 6T-SRAM cell design (considering parasitic effects) achieves a reduction of 81.60% in Write Dynamic Power and 64.65% in Write Time; HSNM and RSNM are improved by 11.92% and 6.42%, respectively. The optimized 7 nm FinFET 6T-SRAM cell structure in this paper outperforms the parasitic-free structure in terms of the performance parameters above, even when taking into account parasitic effects.

Source: Electronics

DOI: 10.3390/electronics14204002

Authors: Qiwen Zheng, Ye Wu, Chun Zhao, Jiafeng Zhou

Institutions: Xi’an Jiaotong-Liverpool University, University of Liverpool

Language: English

Topic: Low-power high-performance VLSI design

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896