مجال التميز | تميز دراسي وبحثي |
البحوث المنشورة |
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البحث (1): | |
عنوان البحث: |
Online Remaining Useful Lifetime prediction using support vector regression |
رابط إلى البحث: |
https://ieeexplore.ieee.org/document/9522047 |
تاريخ النشر: |
25/08/2021 |
موجز عن البحث: |
An accurate prediction of remaining useful lifetime (RUL) in high reliability and safety electronic systems is required due to its wide use in industrial applications. In this paper, we propose a novel methodology for online RUL prediction, using support vector regression (SVR) model. Through Cadence simulations with 22nm CMOS technology library, we demonstrate that frequency degradation follows a trackable path and depends on temperature, voltage and aging. This characteristic is exploited for training the SVR model, validated over 20 years of aging degradation. Our methodology is capable of highly accurate RUL estimation, requiring a ring oscillator (RO), temperature sensor and trained SVR software model. Using a supply voltage of 0.9 V and variation in temperature from 0C to 100C, 13 and 21 stage RO show 90% cases with a RUL prediction deviation of 0.2 years, and the remaining between 0.75 and 0.8 years, respectively. Furthermore, with voltage variation from 0.7 to 0.9V, with steps of 0.05V and four representative temperatures (25, 50, 75 and 100 C), the 13-RO shows 52% cases between 0.2 years, 21-RO has 80.5% cases concentrated between 0.2 years of RUL prediction deviation and remaining cases for both ROs are located between 0.8 years. |
البحث (2): | |
عنوان البحث: |
Differential Aging Sensor Using Subthreshold Leakage Current to Detect Recycled ICs |
رابط إلى البحث: |
https://ieeexplore.ieee.org/document/9563093 |
تاريخ النشر: |
07/10/2021 |
موجز عن البحث: |
Electronic system components can fall prey to counterfeiting via untrustworthy parties in the semiconductor supply chain, which has established a worldwide span to reduce costs, time to market, and increase productivity. Recently, integrated circuits (ICs) counterfeiting has threatened systems security and reliability that utilize ICs in all domains. This article focuses on the most counterfeited area—recycled and remarked ICs—and aims to develop a technique to distinguish between new and used digital ICs based on an aging sensor mechanism. Aging sensors have been studied based on path-delay fingerprinting and ring oscillators (ROs) frequency degradation, but their resolution requires further development to accurately detect short usage. This study proposes a novel differential aging sensor to measure the discharge time (τdv ) increase that depends on the subthreshold leakage current due to aging with two on-chip designs. Simulations were conducted using the GlobalFoundries (GF) 22 nm for aging with bias temperature instability and hot carrier injection (HCI) combined. The results show that the τdv increase is 14.72% after 15 days of usage and increases to 60.49% after three years. This further increases at higher temperatures; the highest simulated temperature (125°C) τdv increases by 55.93% after 15 days and 310.17% after three years. The proposed method also outperformed the traditional frequency degradation-based aging estimation method, which at nominal temperature is found to be 5.00% after 15 days and 23.68% after three years. Therefore, discharge time is a sensitive indicator for aging, surpasses frequency in detecting previous usage and is robust against process, voltage, and temperature variations (PVTs). |
المؤتمرات العلمية |
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المؤتمر (1): | |
عنوان المؤتمر: |
The 24th Design, Automation & Test in Europe Conference & Exhibition (DATE) 2021 |
تاريخ الإنعقاد: |
04/02/2021 |
مكان الإنعقاد: |
Grenoble, France |
طبيعة المشاركة: |
Poster |
عنوان المشاركة: |
Differential Aging Sensor to Detect Recycled ICs using Sub-threshold Leakage Current |
ملخص المشاركة: |
Integrated circuits (ICs) may be exposed to counterfeiting due to the involvement of untrusted parties in the semiconductor supply chain; this threatens the security and reliability of electronic systems. This paper focusses on the most common type of counterfeiting namely, recycled and remarked ICs. The goal is to develop a technique to differentiate between new and recycled ICs that have been used for a short period of time. Detecting recycled ICs using aging sensors have been researched using sub-threshold leakage current and frequency degradation utilizing ring oscillators (ROs). The resolution of these sensors requires further development to accurately detect short usage time. This paper proposes a differential aging sensor to detect recycled ICs using ring oscillators with sub-threshold leakage current to detect aging effects using bias temperature instability (BTI) and hot carrier injection (HCI) on a 22-nm CMOS technology, provided by GlobalFoundries. Simulation results confirm that we are able to detect recycled ICs with high confidence using proposed technique. It is shown that the discharge time increases by 14.72% only after 15 days and by 60.49% after 3 years’ usage, and outperforms techniques that use frequency degradation only, whilst considering process and temperature variation. |
المؤتمر (2): | |
عنوان المؤتمر: |
The 4th International Conference on Computing & Information Sciences (ICCIS) 2021 |
تاريخ الإنعقاد: |
29/11/2021 |
مكان الإنعقاد: |
Karachi, Pakistan |
طبيعة المشاركة: |
Paper |
عنوان المشاركة: |
A Support Vector Regression based Machine Learning method for on-chip Aging Estimation |
ملخص المشاركة: |
Semiconductor supply chain industry is spread worldwide to reduce cost and to meet the electronic systems high demand for ICs, and with the era of internet of things (IoT), the estimated numbers of electronic devices will rise over trillions. This drift in the semiconductor supply chain produces high volume of e-waste, which affects integrated circuits (ICs) security and reliability through counterfeiting, i.e., recycled and remarked ICs. Utilising recycled IC as a new one or a remarked IC to upgrade its level into critical infrastructure such as defence or medical electronics may cause systems failure, compromising human lives and financial loss. This paper harvests aging degradation induced by BTI and HCI, observing frequency and discharge time affected by changes in drain current and sub-threshold leakage current over time, respectively. Such task is undertaken by Cadence simulations, implementing a 51-stage ring oscillator (51-RO) using 22nm CMOS technology library and aging model provided by GlobalFoundries (GF). Machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, discharge time, frequency, and aging time. The data sampling is performed over an emulated 12 years period with four representative temperatures of 20° C, 40° C, 60° C, and 80° C with additional testing data from temperatures of 25° C and 50° C. The results demonstrate a high accuracy on aging estimation by SVR, reported as a normal distribution with the mean (µ) equal to 0.01 years (3.6 days) and a standard deviation (σ) of ±0.1 years (±36 days). |
تركي بن مبروك حسين النعيري
دكتوراه
هندسة
University of Liverpool