Artificial neuromodulator–synapse mimicked by a three-terminal vertical organic ferroelectric barristor for fast and energy-efficient neuromorphic computing

Abstract

Novel structures for synaptic devices and innovative array configurations are crucial for implementing fast and energy-efficient neuromorphic electronics. We introduce a three-terminal vertical organic ferroelectric barristor equipped with synaptic functions based on Schottky barrier height modulation to implement a neural network with parallel concurrent execution. The barristor can be extended to a diagonal neural network array while sustaining a crossbar array with nondestructive cell programming given the vertical stacking of layered gate line patterning on top. The array enables fast and energy-efficient operation of a diagonal convolutional neural network (CNN) that performs simultaneous weight update of cells sharing a kernel matrix. One-step convolution and pooling can be achieved, omitting sequential convolution for extracting and storing feature maps. The energy for vector–matrix multiplication on the MNIST and Clothes datasets using the diagonal CNN can be reduced by 75.80% and 71.79%, respectively, compared with the use of a conventional CNN structure while reducing the number of image sliding operations to one-fourth and achieving similar recognition accuracy of ∼91.03%.