PhD student in Electrical and Computer Engineering at the University of British Columbia under the supervision of Professor Andre Ivanov. His research interests include hardware accelerators, device and circuit reliability, computer-aided design (CAD), and machine learning (ML).
He is currently focused on developing an ML solution for predicting transistor aging – both in short-term and long-term – due to phenomena such as: bias temperature instability (BTI), hot carrier injection (HCI), and time-dependent dielectric breakdown (TDDB). The aim is for the ML model to meet three objectives:
- to predict short-term aging faster than the analytical, physics-based models provided by the foundry (which are also confidential).
- to be flexible enough that it could mimic the behavior of the physics-based models under different stress conditions (e.g., different temperatures), without the need to be re-trained from scratch.
- to predict the timing degradation of a circuit in long-term with good accuracy, by extrapolating its short-term aging predictions.
In his free time, he enjoys playing video games and exploring the beautiful city of Vancouver.