Yundong Xuan

This work investigates the effects of total ionizing dose (TID) on 22 nm FDSOI MOSFET devices through combined technology computer-aided design (TCAD) simulations and actual TID irradiation experiments, establishing a comprehensive dataset of radiation-induced degradation characteristics. By utilizing both simulated and experimental radiation data as training foundations, this research developed and optimized a deep neural network (DNN) capable of conducting efficient and accurate reliability analysis of TID effects under various radiation conditions, with demonstrated effectiveness in predicting device performance degradation across different TID ranges. The methodology integrates physical simulations with deep learning approaches, where TCAD simulations and irradiation experiments capture fundamental device physics under radiation exposure, while the DNN enables rapid performance prediction. The resulting framework achieves superior prediction accuracy while significantly reducing computational costs compared to conventional simulation methods, providing a powerful solution for evaluating electronic device reliability in complex radiation environments.

This work employs Technology Computer-Aided Design (TCAD) simulations to systematically investigate the total ionizing dose (TID) effects, single-event transient (SET) effects, and their coupling mechanisms in 22 nm fully depleted silicon-on-insulator (FDSOI) technology. In the TID effects study, FDSOI NMOS devices were simulated using fixed charge model and gamma radiation model separately. The results demonstrate that TID irradiation leads to a reduction in threshold voltage and a significant increase in drain current. Applying a negative back-gate bias effectively mitigates this performance degradation, with devices exhibiting higher radiation sensitivity under OFF-state bias conditions. For the SET effects investigation, the influences of back-gate bias, operating temperature, and linear energy transfer (LET) values on device transient response were systematically examined. The findings indicate that a negative back-gate bias significantly suppresses SET effects, while both the transient current amplitude and collected charge increase substantially with rising temperature and LET values. Furthermore, the impact of TID irradiation on the SET response of both individual devices and a three-stage inverter chain was investigated. Results reveal that TID irradiation not only reduces the SET susceptibility of single NMOS transistors but also effectively suppresses the broadening of SET pulses at each output node of the threestage inverter chain. These findings demonstrate significant interactions between TID and SET effects at both device and circuit levels.

This study investigates the impact of the self-heating effect (SHE) on single-event transient (SET) sensitivity in triple-layer stacked nanosheet transistors, using technology computer-aided design (TCAD) simulations. The results demonstrate that SHE significantly elevates the channel lattice temperature under DC bias, leading to notable degradation in DC performance metrics, including the drive current (I_ON) and the on/off current ratio. By employing a finer time resolution in the AC simulation, we observed that the device reaches thermal equilibrium on a picosecond timescale. Crucially, SHE is found to exacerbate SET sensitivity markedly. Compared to simulations without SHE, the presence of self-heating increases both the peak transient current and the collected charge at the drain terminal following heavy-ion strikes. Furthermore, the transient response is shown to depend on the thermal history; longer pre-strike heating times amplify the SET peak magnitude, whereas longer cooling times attenuate it. These findings underscore the critical importance of co-optimizing thermal management and radiation hardening in the design of advanced nanosheet technologies.

In this work, the effects of heavy ion strike position, incident angle, linear energy transfer (LET) value, ambient temperature, bias conditions, and the synergistic effects of total dose irradiation on the single-event transient (SET) in silicon-germanium heterojunction bipolar transistors on silicon-on-insulator (SiGe-on-SOI HBTs) were investigated using TCAD simulations. It was demonstrated that, compared to the bulk SiGe HBT, the SiGe-on-SOI HBT exhibits lower transient current and less charge collection, indicating better resistance to SET. The SET response is more pronounced when heavy ions strike vertically from the emitter and base regions. Transient current and collected charge escalate with increasing incident angle, demonstrating a strong linear correlation with LET values. As the temperature decreases, the peak transient current increases, while the pulse duration decreases and the total collected charge diminishes. After total dose irradiation, the peak transient current in the SiGe-on-SOI HBT decreases, whereas the damage was more severe in the absence of irradiation. Under collector positive bias and positive bias, significant SET responses were observed, while cutoff bias and substrate bias exhibited better resistance to SET damage. These findings provide critical insights into radiation-hardened design strategies for the SiGe-on-SOI HBT.

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies.