Harnessing radiation-induced fluctuations in spintronic neuromorphic hardware for energy-efficient aerospace computing

The integration of artificial intelligence into space systems faces fundamental challenges in radiation resilience and energy-efficient computation. Here we present a neuromorphic computing advance that proposes a strategy to transform these challenges into computational advantages via spintronic technology. Our developed spin-orbit torque magnetic tunnel junctions crossbar array harnesses radiation-induced fluctuations to enhance Hopfield neural network optimization, converting an environmental constraint into a functional benefit. When exposed to heavy-ions (e.g., 209Bi23+), the system demonstrates remarkable radiation hardness with only 1.04% tunneling magnetoresistance degradation while maintaining operational stability. Implemented in a 4 Kb array, this neuro-inspired architecture solves the 8-city traveling salesman problem with 95.2% accuracy at 45.06 nJ energy consumption - outperforming conventional radiation-hardened approaches. Such complementary experimental and simulation approach elaborates that the measured irradiation conductance fluctuations can be mapped to synaptic weights in a Hopfield network model, significantly enhancing its optimization capability. This work corroborates a new paradigm for adaptive, energy-efficient nanoscale artificial intelligence hardware that is designed to thrive in extreme environments, with implications for radiation-resilient neuromorphic architectures and edge computing.