Topic

The performance and survivability of Department of War (DoW) and space systems, especially those operating in space environments, are critically affected by radiation. Radiation testing of parts are major cost and schedule drivers for DoW and space systems. This topic seeks to conduct a rigorous statistical analysis of long-term radiation trends and their impact with the goal of either reducing or justifying the need for rigorous and costly radiation testing. DoW systems must determine a part’s susceptibility to many forms of radiation including but not limited to: neutron, proton, heavy ion, displacement damage, total ionizing dose. Successful proposals may focus on all or some subset of these radiation environments and would seek innovative approaches to analyze historical radiation data from various sources in the open literature, including but not limited to: Archived space-based radiation measurements (e.g., from past and current satellite missions). Data from ground-based radiation testing facilities and simulation environments. Historical performance data of space components and subsystems exposed to radiation. Legacy data of previous test failures to analyze and attribute the event to the natural space environments. Proposers should be able to conduct their analysis without data provided by the Missile Defense Agency. The analysis should focus on: Trend Identification: Identifying long-term trends and patterns in radiation effects of microelectronics. For example, the development of a-priori expectations based on part type, node size and process technology, and trends in lot-to-lot variation of radiation performance for particular devices. Correlation Studies: Correlating historical radiation data with observed performance degradation of sensors, electronics, and other critical spacecraft components. This includes exploring the relationships between radiation dose, single event effects (SEUs), total ionizing dose (TID), and other radiation-induced phenomena, with failures or degradation of space systems. Predictive Modeling: Developing predictive models that use historical radiation data to forecast the radiation environment impact on future and current microelectronics devices and process technologies. This may involve incorporating machine learning techniques to identify complex relationships and improve prediction accuracy. Uncertainty Quantification: Quantifying the uncertainties associated with historical data and predictive models and assessing their implications for risk management, decision-making and design margin.