Reconfigurable Selector‐Only Memory (SOM) for Scalable Neuromorphic Computing

ABSTRACT Highly scalable reconfigurable neuromorphic devices are critical for addressing continual‐learning challenges in artificial intelligence. However, the scalability of existing reconfigurable devices is severely constrained by limited operating margins and insufficient process maturity. Here, we propose selector‐only memory (SOM) as a scalable device candidate. Its volatile threshold switching and programmable nonvolatile threshold window are operationally decoupled, and it is compatible with in‐line fabrication and 3D stacking. We demonstrate an In‐doped GeSe SOM that enables neuron–synapse reconfigurability within a single cell. By leveraging intrinsic parasitic capacitance, we implement a capacitor‐free leaky integrate‐and‐fire neuron and validate all‐or‐none firing, integrate‐and‐fire dynamics, and input‐controlled firing‐rate modulation using experiments and an equivalent model. For synapses, we propose a one‐shot subthreshold‐conductance readout method. With a unified reverse‐subthreshold pulse scheme, 16 programmed conductance states are obtained through one‐shot subthreshold readout, and most states remain distinguishable over 10 ⁴ s. Finally, SOM‐parameter‐based simulations on a Growing‐When‐Required MNIST task achieve 2.67× higher accuracy with 70% of the nodes and shrink to 58% after rollback. These results indicate that SOM provides a promising selector‐derived device concept for scalable reconfigurable neuromorphic hardware.