Volume 22, Issue 1 (JIAEEE Vol.22 No.1 2025)
Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(1): 65-77 |
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Bazli M, Es'haghi S, Shahi M, Naseh M. Simultaneous analysis of the stochastic transistor aging and process variation in digital systems by developing machine learning-based models for standard cells. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (1) :65-77
URL: http://jiaeee.com/article-1-1601-en.html
URL: http://jiaeee.com/article-1-1601-en.html
Bijand Branch - Islamic Azad University
Abstract: (716 Views)
Nowadays, reliability is one of the most important challenges in the design of nanometer circuits. Transistor aging and process variation are two important factors affecting reliability. After the circuits start to work, transistors age gradually, which means that the characteristics of transistors and in particular their threshold voltage degrade. As a result, delay increases and eventually causes violation of timing constraints and incorrect operation of the circuit. Various phenomena cause the transistor aging, among them BTI is the most important. This phenomenon reveals has stochastic nature in today's advanced manufacturing technologies, and atomic models exist to describe it. Process variation due to the non-ideality of the manufacturing process and often under the influence of the lithography process appears as a stochastic difference in the characteristics of the transistors produced with the expected values after manufacturing. The simultaneous analysis of these phenomena is vital to improve the design. Today, the starting point of digital systems design has raised to higher abstraction levels. As a consequent, we need fast and accurate models to evaluate the reliability of circuits in order effective exploration of the design space. In this article, first, machine learning based models are developped for standard cells. Then, a Monte Carlo-based analysis method is proposed for the simultaneous analysis of aging and process variation. The experimental results show an improvement in analysis time with an average of 94% compared to existing methods. This improvement has been achieved while maintaining the accuracy of the analysis. Of course, to achieve this improvement, a lot of time is spent on training the models, which is done once and offline and has no effect on the execution time.
Type of Article: Research |
Subject:
Electronic
Received: 2023/05/22 | Accepted: 2024/09/10 | Published: 2025/05/29
Received: 2023/05/22 | Accepted: 2024/09/10 | Published: 2025/05/29
Extended Abstract [PDF 267 KB] (3 Download)
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