DISSERTATION DEFENSE: DEVON RICHMAN

Author: Devon Richman

Date/Time: February 25th, 2026 at 11:00am
Location: EGR-2164 | Zoom

Committee Members:

Dr. Michael Pecht, Chair

Dr. Michael Azarian, Co-chair

Dr. Abhijit Dasgupta

Dr. Patrick McCluskey

Dr. Peter Sandborn

Dr. Ankur Srivastava, Dean’s Representative

Title: DEGRADATION ASSESSMENT OF MICROELECTRONIC DEVICES USING SIDE-CHANNEL POWER MODULATION ANALYSIS

Abstract: 

Microelectronic devices degrade over time due to exposure to stress, including temperature, voltage, and ionizing radiation. Rapid and nondestructive methods for detecting and quantifying degradation are increasingly important as our lives become more reliant on electronics, but conventional electrical testing approaches are often time-intensive or lack sensitivity to early-stage degradation.
Side-channels are indirect sources of information that originate from a device and contain information about its physical or electrical state. While side-channel analysis has been widely studied in the cryptography and security domains, its application to reliability and quality assessment has remained limited. This dissertation presents a side-channel approach, power modulation analysis (PMA), for the rapid detection and quantification of degradation in microelectronic devices.

A feature extraction methodology along with a suite of engineered features is developed to improve traceability, dataset independence, and domain relevance relative to existing PCA-based approaches. The proposed method is evaluated on devices that are degraded by being subjected to high-temperature bias stress and total ionizing radiation, demonstrating the ability to detect and quantify multiple stages of degradation.

In addition, PMA is applied to the screening of counterfeit microelectronic devices. Counterfeiting microelectronic devices is an ever-growing threat to the reliability and security of electronic systems. Current standards-based testing methods are time-intensive and require numerous test methods to be performed. The results show that engineered features provide more accurate and repeatable discrimination than PCA-based approaches, enabling faster and more consistent screening. This work expands the use of side-channel analysis beyond security applications and establishes PMA as a practical tool for microelectronic reliability and quality assessment.