The gate injection-based field-effect synapse transistor with linear conductance update for online training

Abstract

Neuromorphic computing, an alternative for von Neumann architecture, requires synapse devices where the data can be stored and computed in the same place. The three-terminal synapse device is attractive for neuromorphic computing due to its high stability and controllability. However, high nonlinearity on weight update, low dynamic range, and incompatibility with conventional CMOS systems have been reported as obstacles for large-scale crossbar arrays. Here, we propose the CMOS compatible gate injection-based field-effect transistor employing thermionic emission to enhance the linear conductance update. The dependence of the linearity on the conduction mechanism is examined by inserting an interfacial layer in the gate stack. To demonstrate the conduction mechanism, the gate current measurement is conducted under varying temperatures. The device based on thermionic emission achieves superior synaptic characteristics, leading to high performance on the artificial neural network simulation as 93.17% on the MNIST dataset.

The conventional von Neumann computing architecture is ill suited to data intensive tasks as data must be repeated moved between the separated processing and memory units. Here, Seo et al propose a CMOS compatible, highly linear gate injection field-effect transistor where data can be both stored and processed.