SBIR/STTR Award attributes
Radiation effects in microelectronics are a significant concern for DoD systems that operate at high altitudes or in outer space. Typical characterization efforts focus on macroscale degradation signatures from electrical measurements at device terminals. However, a comprehensive analysis of radiation-induced physical defects is not possible based solely on terminal measurements. CFD Research and Arizona State University propose a predictive modeling effort to complement detailed experiments for addressing this challenge. We will perform multiscale physics-based modeling of the radiation response of a selected semiconductor device, and use it with the electrical characterization data to guide Transmission Electron Microscopy-based nanoscale material characterization. We will utilize device simulation and measurement data to develop Artificial Intelligence/Machine Learning-based predictive models for quantitative correlation of the radiation-induced nanoscale material defects with macroscale electrical measurements. In Phase I, we successfully performed a feasibility study using multiscale electrical and material characterization of a simple device structure and a relevant radiation effect, while using the data to develop predictive behavioral models for the radiation effect. In Phase II, we will further develop and apply the predictive modeling and experimental method on two different technologies and radiation effects, and demonstrate its benefit towards developing radiation-tolerant electronics for DoD missions.
