@inproceedings{44f40b42e3514f12a9a6a0d5686308c8,
title = "Inverse Design of FinFET SRAM Cells",
abstract = "A convenient method based on deep neural networks and an evolutionary algorithm is proposed for the inverse design of FinFET SRAM cells. Inverse design helps designers who have less device physics knowledge obtain cell configurations that provide the desired performance metrics under selected wearout conditions, such as a set specific stress time and use scenario that creates a specific activity level (duty cycle and transition rate). The cell configurations being considered consists of various process parameters, such as gate length and fin height, in the presence of variations due to process and wearout. The front-end mechanisms related to wearout include negative bias temperature instability (NBTI), hot carrier injection (HCI), and random telegraph noise (RTN). The process of inverse design is achieved quickly and at good accuracy.",
keywords = "FinFET, HCI, Inverse Design, NBTI, Performance Metrics, RTN, SRAM, Wearout Mechanisms",
author = "Rui Zhang and Zhaocheng Liu and Kexin Yang and Taizhi Liu and Wenshan Cai and Linda Milor",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Reliability Physics Symposium, IRPS 2020 ; Conference date: 28-04-2020 Through 30-05-2020",
year = "2020",
month = apr,
doi = "10.1109/IRPS45951.2020.9129530",
language = "English",
series = "IEEE International Reliability Physics Symposium Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Reliability Physics Symposium, IRPS 2020 - Proceedings",
address = "United States",
}