Synaptic Characteristics of Fully Depleted Silicon-on-Insulator Metal-Oxide-Semiconductor Field-Effect Transistors and Synapse-Neuron Arrayed Neuromorphic Hardware System

Abstract

A fully depleted silicon-on-insulator (FDSOI) metal-oxide-semiconductor field-effect transistors (MOSFETs) device is investigated as for an electronic synapse emulating the synaptic functions of the human brain with stable characteristics. Gate-last processed FDSOI MOSFET with a high-k/metal gate stack features a memory window of 103. Synaptic conductance is stably regulated by utilizing the FDSOI MOSFET, which offers the advantage of mitigating leakage current when compared to bulk Si MOSFET. Short- and long-term plasticity are investigated by applying engineered pulse, verifying the long-term synaptic properties of pattern recognition processes. With controllable synaptic conductance, the trade-off between conductance change and linearity regarding the recognition rate is evaluated, attaining a recognition rate of 0.83. To verify the pre- and post-synaptic weights within a real hardware-based neuromorphic system, 5 × 6 FDSOI field-effect transistor (FET) synapse array is interconnected to 10 × 10 leaky integrate-and-fire (LIF) neuron array. The synaptic plasticity of FDSOI MOSFET in post-neurons following neuron firing in the neuron device is successfully demonstrated. These results indicate that FDSOI MOSFET devices could be applicable as synapse devices due to controllability and capability to realize signal transmissions and self-learning processes simultaneously and used to mimic a synapse neuron network system by configuring a hardware system interconnected with the LIF neuron.