PhD - Leuven | More than two weeks ago
Recently, there has been a tremendous advancement in the domain of wireless/mobile communication technology, with the recent introduction of 5G and RF standards for IoT and near field communication in the frequency range of 30 – 300 GHz and higher. The requirement of high scalability and low-power consumption is driving the research focus towards the use of advanced GaN based HEMT/MISHEMT devices and InP-based Heterojunction Bipolar Transistors (HBTs).
One of the major hurdles of the large-scale integration of these high-performance RF devices in the standard CMOS process flow is their poor reliability and operating lifetime. The defects present in various sections of the device (channel, barrier/capping layer, heterojunctions and surface layers and buffer layers) result in time-dependent degradation of the electrical characteristics of devices, and could eventually lead to their breakdown. Additionally, the AC characteristics such as threshold and maximum oscillation frequency (ft/fmax), parasitic losses and dynamic on-Resistance (RON), Pout and PAE at quiescent operating conditions are also severely impacted. Therefore, obtaining a fundamental understanding of the kinetics of charge trapping in the defects is crucial for enabling the future 6G/mmWave technologies.
This PhD will focus on the reliability characterization and (semi-)empirical modeling of device reliability. The impact of defects on device operation will be studied in detail and industry-relevant solutions to overcome the major reliability concerns may be explored. Different characterization techniques, such as Hot-Carrier Degradation (HCD), TDDB (Time-Dependent Dielectric Breakdown) characterization, device RON dispersion measurements and Pout/PAE degradation monitoring techniques should be employed to efficiently characterize and model the various reliability mechanisms.
Required background: Master’s degree in Electrical Engineering, Nanoscience and Nanotechnology or equivalent
Type of work: Literature study: 20%, Experimental: 50%, Modelling: 30%
Supervisor: Piet Wambacq
Co-supervisor: Bertrand Parvais
Daily advisor: Vamsi Putcha
The reference code for this position is 2021-059. Mention this reference code on your application form.