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

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 TiO₂ 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.