Piezo‐Phototronic Transistors Amplified by Magnetic Fields for Neuromorphic Feature Extraction and Associative Learning

ABSTRACT Brain‐inspired neuromorphic computing hinges on multidimensional regulation of devices, whereas piezo‐phototronic devices with distinct advantages in such capability remain significantly underexplored in this frontier domain. Here, monolithic integration of neuromorphic perception, associative learning, and feature extraction is achieved in a novel magnetostrictive‐amplified piezo‐phototronic transistor (MAPP‐FET), which incorporates a Terfenol‐D substrate with sequentially stacked mica, graphene, HBN, MoTe 2 , and α‐In 2 Se 3 layers. Under magnetostrictive stimulation, the MAPP‐FET demonstrates a remarkable excitatory postsynaptic current enhancement factor of 1315.7 and a paired‐pulse facilitation index of 177.25% (112.81% without magnetic field), revealing substantially enhanced synaptic plasticity. Based on these remarkable synaptic characteristics, we have not only successfully simulated human emotional associative memory behavior co‐induced by light and magnetism, but also achieved a 4100% improvement in image recognition efficiency in feature extraction tasks combined with an artificial neural network (ANN). This work further expands the application scope of piezo‐phototronics in neuromorphic computing and establishes a new design paradigm for intelligent hardware capable of brain‐like perception and learning.