From satellites exploring deep space to aircraft crossing continents to data centers powering AI, electronics must survive constant bombardment from radiation. Cosmic rays, solar particles, and terrestrial neutrons can flip bits, crash systems, or cause permanent failures.
Modern systems present a growing challenge: they’re too complex to test comprehensively. We can’t probe buried layers in 3D chips or access individual components in heterogeneous packages. Testing even a small fraction of possible failure scenarios is beyond reach.
Our research develops new approaches to this problem by combining high-throughput laser experiments with machine learning to predict radiation effects in systems we can no longer test directly.
What We Do
High-Throughput Testing — We generate large data sets by designing novel experimental approaches, enabling new insights impossible with sparse measurements.
AI-Driven Analysis — We use machine learning to discover which design features predict radiation failures, extracting principles from large experimental datasets.
Predictive Frameworks — We build multi-scale models that enable radiation-hardened design for complex systems that can’t be tested directly.
Impact
🛰️ Space systems that survive harsh radiation environments
🖥️ Terrestrial computing protected from cosmic ray errors
✈️ Critical electronics for aerospace and high-reliability applications
Join Our Research
PhD Students: We’re looking for motivated students who want to work at the intersection of experimental physics, machine learning, and electronic design. You’ll build expertise from device physics to AI while solving critical problems for space and terrestrial systems.
Collaborators: We partner with national laboratories, industry, and academic institutions on radiation effects research for next-generation systems.
Contact
Adrian Ildefonso, Ph.D.
📧 aildefon@iu.edu