Cavity-Enhanced High Sensitivity InP Nanosheet Optoelectronic Synaptic Transistors for Neuromorphic Computing

Optoelectronic synaptic transistors that combine sensing and computing functionalities are emerging as key components for next-generation neuromorphic systems, yet their scalability and performance remain limited by device architecture and fabrication constraints. Here, we report cavity-enhanced optoelectronic synaptic transistors based on surface-state modulation in selectively grown InP nanosheets. Benefiting from optical-cavity-induced high gain and effective dark-current suppression, the device exhibits an ultrahigh responsivity of approximately 1.5 × 10⁶ A W⁻¹, a specific detectivity of ≈5 × 10¹⁴ Jones, and a noise-equivalent power of ≈10⁻¹⁶ W Hz^−1/2. Precise control of charge dynamics at surface states using electrical and optical stimuli allows the emulation of key synaptic functions, including short- and long-term plasticity, paired-pulse facilitation, and learning–forgetting–relearning behaviors. Furthermore, convolutional neural network simulations leveraging the device's continuously tunable conductance yield image recognition accuracies approaching 90%. These results establish InP nanosheet optoelectronic synaptic transistors as a scalable and high-performance platform for integrated neuromorphic computing.