Four-Terminal Ferroelectric Schottky Barrier Field Effect Transistors as Artificial Synapses for Neuromorphic Applications

In this paper, artificial synapses based on four terminal ferroelectric Schottky barrier field effect transistors (FE-SBFETs) are experimentally demonstrated. The ferroelectric polarization switching dynamics gradually modulate the Schottky barriers, thus programming the device conductance by applying negative or postive pulses to imitate the excitation and inhibition behaviors of the biological synapse. The excitatory post-synaptic current can be modulated by the back-gate bias, enabling the reconfiguration of the weight profile with high speed of 20 ns and low energy (< 1 fJ/spike) consumption. Besides, the tunable long term potentiation and depression show high endurance and very small cycle-to-cycle variations. Based on the good linearity, high symmetricity and large dynamic range of the synaptic weight updates, a high recognition accuracy (92.6%) is achieved for handwritten digits by multilayer perceptron artificial neural networks. These findings demonstrate FE-SBFET has high potential as an ideal synaptic component for the future intelligent neuromorphic network

Heterosynaptic Plasticity and Neuromorphic Boolean Logic Enabled by Ferroelectric Polarization Modulated Schottky Diodes

Abstract Neuromorphic computing employs a great number of artificial synapses which transfer information between neurons. Conventional two‐ or three‐terminal artificial synapses with homosynaptic plasticity suffer from a positive feedback loop problem. Synapses with heterosynaptic plasticity are thus required to perform learning, processing and modulating simultaneously. Here, complementary metal‐oxide‐semiconductor compatible artificial synapses based on ferroelectric polarization modulated Schottky diodes (FEMOD) on silicon, which enables heterosynaptic plasticity with multi‐functionalities, high endurance, low power consumption, and high speed, are presented. High accuracy is obtained in the supervised learning simulation of artificial neural networks due to the large number of conductance states, good linearity, and small variations of FEMOD synapses. Boolean functions are demonstrated with only one or two FEMOD devices operating at low voltage and low power consumption. The proposed device structure performs multi‐functions of biological synapse and Boolean logic, thus provides high potential for the future large scale and low power neuromorphic computing applications