Crystallinity‐Programmed Memristive Devices Enable Reconfigurable Neuromorphic Sensing With Hardware VMM Readout

ABSTRACT In‐sensor reservoir computing offers a promising paradigm for signal analysis by embedding sensing and computation within a single platform. However, it remains challenging to realize both dynamic temporal processing and long‐term memory using a single device. Here, we report a multi‐modal and reconfigurable oxide‐based memristive device that enables both volatile and nonvolatile switching modes in a unified architecture. By precisely tuning the crystallinity of the TiO 2 layer and adjusting the compliance current, we modulate the conductive filament dynamics to switch between volatile and nonvolatile behavior, and multi‐modal switching is verified based on nucleation theory. The volatile mode enables fading memory and nonlinearity required for high‐dimensional temporal encoding, while the nonvolatile mode provides robust analog weight storage with 5‐bit resolution and retention exceeding 10⁵ s. These dual functions are integrated into a neuromorphic in‐sensor reservoir computing system. The system accurately reconstructs ECG waveforms (NRMSE = 0.010) and achieves multi‐step prediction of pH time‐series (accuracy = 98.2%), while reducing energy consumption by over five‐fold compared to conventional echo state networks. We demonstrate a scalable and energy‐efficient approach toward intelligent biochemical sensing, highlighting how material‐level configurability in memristive devices can unlock new directions for on‐sensor neuromorphic hardware.