Hysteresis-aware MEMS neuromorphic networks for embedded sensing and computation

Microelectromechanical systems (MEMS) provide a platform where sensing, memory, and computation can co-exist within the same physical substrate. Here, we present a hysteresis-aware MEMS-based Continuous-Time Recurrent Neural Network (MEMS-CTRNN) that transforms nonlinear bistability from a device limitation into a computational resource. By embedding hysteresis into the training process, the MEMS-CTRNN achieves temporal memory and noise robustness with threefold fewer parameters than digital Long Short-Term Memory (LSTM) models, while maintaining comparable accuracy. Using insect-scale robotic flight data for validation, we demonstrate collision detection at 93% accuracy, establishing MEMS devices as viable analog recurrent processors. This framework suggests a pathway for MEMS-based neuromorphic architectures that unify sensing and computing for applications spanning microrobotics and in situ biomedical monitoring.