Polarity‐Controlled Volatile HfO 2 Memristors with Bimodal Conductance for Neuromorphic Synapses and Reservoir Computing

In this work, an HfO₂-based memristor exhibiting bimodal switching, wherein the device's conductance is modulated not only by the input stimulus but also by the polarity of the read voltage, is introduced. Uniquely, this device demonstrates reliable short-term memory (STM)-like behavior and supports 16 well-separated conductance states through 4-bit pulsed inputs. Remarkably, under the same input conditions, reversing the polarity of the read voltage results in 16 more different conductance states, thereby doubling the number of levels that can be distinguished per cell. Employing the proposed device, a reservoir computing (RC) system, which takes advantage of this rich representational capability, is created. The system achieves a high classification accuracy of 98.81% on the MNIST dataset. These results show how powerful memristor-based architectures can be and how this device could be a compact and energy-efficient platform for the next generation of neuromorphic computing.