The objective of this PhD is to investigate the physical operation of vertical and semi-vertical GaN devices. The student will be expected to gain a deep fundamental understanding of the physical processes underlying the device operation. This study will be driven through an approach which combines electrical characterization, physical simulation, reliability testing and the study of the interaction between the process and the device operation.
The vertical GaN devices are meant for high voltage and high power operation and are thus subject to high electrical fields and currents. The candidate will be expected to study the operation of the device in the different operational regimes. In the off-state the material will be subject to high electrical fields; through electrical characterization and simulation the candidate will need to identify the regions where breakdown occurs and design/develop novel structures to maximize the achievable breakdown voltage. Another important topic is the gate of a transistor device. Defects, impurities and interfaces states will need to be studied to understand the instabilities which can occur in the vertical trench gate. A further topic of investigation are hot electron effects, which occur in the combined presence of high electrical fields and currents. Characterization techniques and models will need to be developed to explore the device operation in this regime.
Typical techniques and tools which the candidate will need to apply during the course of the PhD, will be on-wafer electrical characterization through current (I-V), capacitance (C-V) and time based measurements. Software tools such as python will be used for automatic data collection and analysis. The main simulation tool for investigating the device physics will be a technology computer aided design (TCAD) tool. The reliability investigation will mainly be done through specific reliability and (capacitive) stress measurements.
Required background: Electrical Engineering, Physics
Type of work: Literature (10%). Modeling (20%), Technology (20%), Experimental (50%)
Supervisor: Benoit Bakeroot
Daily advisor: Shuzhen You
The reference code for this position is 2020-082. Mention this reference code on your application form.