Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing

This work highlights the application of a junctionless (JL) transistor with charge trapping mechanism as an artificial synaptic device for neuromorphic computing. In this work, synapse behaviors ((short-term potentiation (STP), long-term potentiation (LTP), and depression (LTD))) have been validated and analyzed by storing the positive charges (holes) in the floating body and charge trapping nitride layer. JL device can be operated at a lower drain voltage (V
DS
= 0.8 V) to trigger the band-to-band tunneling and impact ionization mechanisms. The device achieves a higher and linear conductance value, and the non-linearity value for LTP is 0.1, which is beneficial for neural networks. Estimated conductance values from the device are utilized to estimate the pattern recognition and achieve an accuracy of ~ 85 % with the CNN algorithm and CIFAR-10 datasets.