Candidate: Tonghe Bai
Date: November 19, 2024
Time: 9:30am
Location: online
- Meeting ID: 255 920 186 873
- Passcode: oMxE9d
Supervisor: Na Young Kim
All are welcome!
Abstract:
This thesis proposes a multi-stage, robust framework for analyzing Random Telegraph Signals (RTS) characterized by temporal fluctuations within semiconductor research, tailored to applications across nanoscale solid-state technologies. The framework provides a scalable, adaptable approach for processing RTS data, a growing necessity as quantum and semiconductor technologies progress. Specifically, this method addresses the high precision required to mitigate noise, support real-time monitoring, and enable automated tuning in devices such as quantum dots (QDs) and some nanoscale CMOS devices where RTS arises from single-carrier actions. Through stages of denoising, feature extraction, and digitization, this framework supports high-resolution analysis of RTS, meeting the complex demands posed by experimental semiconductor data.
The framework is validated with the real-world data of a specific QD holding 2-level RTS, demonstrating a robust 20-fold resolution increase, achieving a time bin reduction from 2 $\mu$s to 100 ns, with further explorations reaching 50 ns. This enhanced resolution uncovers hidden patterns within RTS. By accurately characterizing tunneling rates and transition dynamics, this research yields insights critical for high-fidelity quantum devices, potentially impacting applications like field-programmable gate arrays (FPGAs) and superconducting qubits, where RTS influences operational stability and performance.
Beyond immediate applications, this thesis establishes a flexible RTS processing platform adaptable across various nanoscale semiconductor technologies. Future work will explore broader integration of theoretical and experimental insights to further enhance this framework, creating a versatile toolset aimed at improving robustness and adaptability in semiconductor and quantum devices operating in environments with complex noise and temporal fluctuations.
Keywords: Random telegraph signals, signal denoising, wavelet transforms, kernel density estimation, digitization, semiconductor, quantum dot.