An InGaZnO Synaptic Transistor Using Titanium-Oxide Traps at Back Channel for Neuromorphic Computing

Synaptic transistors have attracted growing interest due to their potential in bio-inspired computing. Conventional synaptic transistors typically rely on charge traps, dipoles, or mobile ions/vacancies in the gate dielectric for channel conductance modulation, which causes performance deterioration owing to Coulomb scattering. Herein, a new dual-gate InGaZnO (IGZO) synaptic transistor is proposed. An unisolated top gate with post metal annealing (PMA) treatment is designed to block moisture and form titanium-oxide (TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>x</roman></sub>)-associated defects at the IGZO back channel. By using the TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>x</roman></sub> defects rather than the gate dielectric to regulate the channel conductance, Coulomb scattering is avoided, and so the device shows relatively high carrier mobility ~11.5 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>2</roman></sup>/(V⋅s) and small subthreshold swing (~231 mV/dec). Additionally, typical biological synaptic functions are successfully mimicked, and a relatively low device-to-device variation (~7.2%) for conductance modulation is obtained. Furthermore, a convolutional neural network (CNN) based on this device achieves high accuracy in the image classification tasks, demonstrating the great potential of the proposed device in neuromorphic computing.