Electrical and Computer Engineering ETDs

Publication Date

Spring 5-16-2026

Abstract

Random telegraph noise (RTN) produces discrete stochastic fluctuations in nanoscale semiconductor devices and increasingly limits performance and reliability as dimensions scale. This dissertation introduces three algorithmic contributions enabling automated and accurate RTN characterization across diverse devices and operating conditions. First, a computationally efficient histogram-based detection algorithm enables rapid identification of RTN in large focal plane array datasets for statistically robust defect analysis. Second, a frequency decomposition framework separates slow and fast RTN components, extending the range of extractable time constants and reducing estimation error in multi-trap signals obscured by background noise. Third, to address the lack of standardized RTN metrics, a quantitative figure of merit is defined to directly compute and compare RTN contributions to total device noise across bias conditions and technologies. Validation on synthetic benchmarks and experimental measurements demonstrates improved scalability, accuracy, and physical interpretability relative to existing techniques.

Keywords

random telegraph noise, signal processing algorithms, defect characterization, time-series analysis, MOSFETs, focal plane arrays

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Francesca Cavallo

Second Committee Member

Ganesh Balakrishnan

Third Committee Member

Sang M. Han

Fourth Committee Member

Christian Morath

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