Project Description

Commercial off-the-shelf microelectronics are appealing for satellite applications because of their high capabilities (e.g., processing speed or memory). While they are generally tested for reliability for terrestrial applications, most manufacturers do not test or qualify them for space applications, leaving a major testing gap to government agencies, which can take millions of dollars to solve for each new device. In this project, we aim to develop an innovative, integrated methodology for predictive modeling and testing of components. This will include developing advanced multi-scale models, from atomic scale to systems, as well as engaging in a suite of test methodologies. Desired outcomes from this work include pioneering methods combining physics and machine learning to provide advanced predictive capabilities in off-the-shelf parts, from both legacy to state-of-the-art processes, and integrated test suite approaches to measure the broadest suite of potential failure modes in the shortest possible time.

Start Date

February 2025 or As Soon As Possible

Postdoc Qualifications

Experience in physics-based modeling of microelectronics. Familiarity with basic machine learning techniques and ability to learn how to write custom scripts. Understanding of fundamentals of space radiation, and basic testing methods in terrestrial environments.

Co-Advisors

Peter Bermel, Allen Garner, Stylianos Chatzidakis, and/or Ashraf Alam

Bibliography

Bermel, Peter, Yiquan Wu, Sylvain Girard, and Juejun Hu. "Photonics for Harsh Environments: introduction to the special issue." Optical Materials Express 13, no. 9 (2023): 2460-2461.
Roy, Sayan, and Peter Bermel. "Investigation of pure and hybrid tungsten-based transition metal di-chalcogenides for radiation resistant space photovoltaic applications." Optical Materials Express 13, no. 8 (2023): 2214-2226.
Niichel, Matthew, and Stylianos Chatzidakis. "Open-Source Optimization of Hybrid Monte-Carlo Methods for Fast Response Modeling of NaI (Tl) and HPGe Gamma Detectors." arXiv preprint arXiv:2406.12903 (2024).
Marquardt, Jeremy, Stylianos Chatzidakis, Allen Garner, and James Prager. "Accelerating Exploration in Plasma and Radiation Physics using Bayesian Optimization." In APS March Meeting Abstracts, vol. 2022, pp. T00-335. 2022.
"Correlated Effects of Radiation and Hot Carrier Degradation on the Performance of LDMOS Transistors", B. K. Mahajan, Y. -P. Chen, U. A. H. Rivera, R. Rahimi, and M. A. Alam, 2022 IEEE International Reliability Physics Symposium (IRPS), pp. P52-1-P52-5, 2022.