Research

Electronics drive nearly every aspect of modern technology, from everyday smartphones to deep-space missions, autonomous vehicles, and AI chat tools. However, when exposed to extreme environments such as radiation, extreme temperatures, and high pressures, these systems can degrade, fail, or behave unpredictably, posing serious challenges for their reliability and longevity.

My group’s research focuses on making microelectronics more reliable in extreme environments. We combine fundamental physics, advanced testing methods, and system-level analysis to understand and prevent failures before they happen. By developing predictive models and radiation-hardening strategies, we ensure that critical electronic systems keep working when failure is not an option.

Current Research Areas

1. Using Lasers to Accelerate Radiation Hardness Assurance (RHA)

Radiation effects testing traditionally relies on particle accelerators, which are costly and difficult to access. One of my areas of research is the use of pulsed lasers to emulate single-event effects (SEEs), which offers a faster, more accessible alternative.

✅ Key Contributions:

• Demonstrating quantitative agreement between laser-based and accelerator-based testing.
• Optimizing laser parameters to accurately replicate heavy-ion-induced single-event effects
• Developing predictive, laser-based testing methodologies for rapid qualification of new semiconductor technologies.

🚀 Impact: This work enables faster screening of space-bound electronics, reducing reliance on scarce accelerator facilities and accelerating the design-to-deployment cycle.

2. Data-Driven Approaches for Radiation Hardening

Machine Learning (ML) has transformed many science fields by enabling powerful data-driven analysis and prediction techniques. However, applying ML to understand radiation effects remains a challenge due to the lack of large-scale experimental datasets. Limited access to particle accelerators makes comprehensive testing to build training data sets difficult. Part of my research focuses on leveraging high-repetition-rate laser testing to generate large datasets for ML-driven analysis of radiation-induced failures.

✅ Key Contributions:

• Training machine learning models to predict SEE-induced failures.
• Developing automated radiation-effects assessment tools that accelerate risk analysis.
• Predicting new circuit and layout topologies to mitigate radiation effects.

🚀 Impact: By integrating ML with experimental techniques, we can streamline the radiation-hardening process and enhance our ability to predict reliability issues at the design stage.

3. Multi-Scale Modeling of Radiation Effects in Electronic Systems

Radiation effects emerge from a multi-scale chain reaction:
☄️ A single charged particle strikes the material, disrupting atoms → ⚡ Generates electrostatic perturbations at the device level → 🖥️ Microelectronic circuits react, potentially leading to malfunctions → 🛰️ Failures propagate through the system, causing unexpected behavior or catastrophic failure.

This challenge is even greater in heterogeneous integration, where different semiconductor technologies (e.g., III-V, SiGe, GaN, CMOS) co-exist within the system, each responding differently to radiation. In highly integrated 3DHI chips, direct testing is extremely difficult—sometimes even impossible—due to their complexity and limited access to internal structures. This makes modeling one of the only viable paths for understanding and mitigating radiation effects.

✅ Key Contributions:

My group is developing a multi-scale modeling framework that connects different levels of abstraction, from atomic interactions to system-level behavior. By integrating:

• Physics-based simulations to capture charge transport and radiation interactions at the device level
• Compact circuit models to translate device responses into circuit-level effects
• Risk-informed reliability analysis to assess how faults propagate across a system
• Multi-scale model integration to link these layers into a predictive, system-aware approach

🚀 Impact: This research enables radiation-aware design strategies by combining multiple modeling techniques in ways that traditional methods cannot. For emerging high-reliability applications like aerospace, defense, and critical infrastructure, this approach provides a path forward where direct testing falls short.

Looking Ahead

The need for radiation-resilient microelectronics has never been greater. As we push the boundaries of space exploration, the demand for electronics that can survive extreme cosmic radiation continues to grow. Satellites, planetary rovers, and deep-space probes rely on microelectronics that must function reliably in harsh environments where failure is not an option.

However, radiation isn’t just a concern for space. With highly scaled technologies, even the weak radiation present on Earth, harmless to humans but strong enough to disrupt modern microelectronics, has become a growing challenge. As data centers expand and AI-driven computing accelerates, radiation-induced errors threaten the reliability of critical infrastructure.

My group is developing next-generation predictive tools and radiation-aware design strategies, combining experimental techniques, AI-driven analysis, and multi-scale modeling. These approaches will not only support next-generation space systems but also ensure the long-term reliability of terrestrial computing.