A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation

Abstract The rapid growth of artificial intelligence and the Internet of Things calls for compact hardware platforms that integrate sensing, computing, and nonlinear processing within a unified architecture. However, most existing neuromorphic systems implement only partial functionalities and rely on heterogeneous device integration, limiting scalability and efficiency. Here, we show a high-speed, reconfigurable multi-modal split-floating-gate memory that monolithically integrates in-sensor computing, in-memory computing, and multiple nonlinear activation functions within a single device structure. By programming charges in spatially separated floating gates, the device enables non-volatile analog control of photoresponsivity and conductance, as well as electrically reconfigurable rectification to emulate ReLU and Sigmoid activations. We further demonstrate a fully hardware-implemented sensor–processor system based on the multi-modal split-floating-gate memory arrays that performs complete unsupervised and supervised learning tasks. This work establishes a compact, energy-efficient, and reconfigurable hardware foundation for scalable intelligent systems beyond conventional silicon architectures.