Name: Varun Khemani
Title: Prognostics and Secure Health Management of Analog Circuits
Committee Members:
Professor Michael G. Pecht, Chair
Dr. Michael H. Azarian, Co-Chair
Professor Peter Sandborn
Professor Abhijit Dasgupta
Professor Mark Fuge
Professor Pamela Abshire, Dean’s Representative

Date: Friday, May 13th, 2022 Time: 01:00 PM Eastern Time (US and Canada)Location: EGR-2164 (ENGR)
Zoom Link:https://umd.zoom.us/j/96565970442?pwd=MU1xMWJyZUJEbS9zS1Zla21EN3hLdz09
Meeting ID: 965 6597 0442
Passcode: 4VrtYb

Analog circuits are a critical part of industrial circuits and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit prognosis and health management (PHM) is critical to the health of industrial circuits. There are a multitude of ways that any analog circuit can fail, which leads to proportional scaling in the number of possible fault classes with the number of circuit components. Therefore, this research presents an advanced design of experiments-based (DOE) approach to account for components that degrade in an individual and interacting fashion, to narrow down the number of possible fault classes under consideration. A wavelet-based deep-learning approach is developed that can localize the circuit component that is the source of degradation and predict the exact value of the degraded component. This degraded value is used in conjunction with physics-of-failure models to predict when the circuit will fail based on the source of degradation.
Increasing outsourcing in the fabrication of electronic circuits has made them susceptible to the insertion of hardware trojans by untrusted foundries. In many cases, hardware trojans are more destructive than software trojans as they cannot be remedied by a software patch and are impossible to repair. Process reliability trojans are a new class of hardware trojans that are inserted through modification of fabrication parameters and accelerate the aging of circuit components. They are challenging to detect through traditional trojan detection methods as they have zero area footprint i.e., require no insertion of additional circuitry. The PHM approach is modified to detect these hardware trojans in order to incorporate circuit security, resulting in the PSHM framework.
Deep neural networks achieve state-of-the-art performance on classification and regression applications but are a black-box approach, which is a concern for implementation. Wavelets are approximations of cells found in the human visual cortex and cochlea. They were used to develop wavelet scattering networks (WSNs), which were intended to be an interpretable alternative to deep neural networks. WSNs achieve state-of-the-art performance on low to moderately complex datasets but are inferior to deep neural networks for extremely complex datasets. Improvements are made to WSNs to overcome their shortcomings in terms of performance and learnability. Further applications of the research are highlighted for rotating machinery vibration analytics, functional safety online estimation.