Reconfigurable Selector-Only Memory (SOM) for Scalable Neuromorphic Computing.

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 104 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.