High-Accuracy Deep Neural Networks Using a Contralateral-Gated Analog Synapse Composed of Ultrathin MoS₂ nFET and Nonvolatile Charge-Trap Memory

Abstract

The development of high-accuracy analog synapse deep neural networks entails devising novel materials and innovative memory structures. We demonstrated an analog synapse with contralateral gates based on a two-dimensional (2D) field-effect transistor and nonvolatile charge-trap memory. Vertical integration of a MoS2-channel FET with a charge-trapping layer provided excellent charge controllability and gate-tunable nonvolatile storage. In the proposed contralateral-gate design, the read and write operations were separated to mitigate read disturb degradation. Reducing the MoS2channel thickness to the ultrathin scale allowed large threshold voltage shifts and on-resistance ( $\text{R}_{\text {ON}}$ ) modulations. This vertically integrated MoS2synapse device exhibited 55 conductance states, high conductance max–min ratio ( ${G}_{\text {MAX}}/ ~{G}_{\text {MIN}}$ ; ~50), low nonlinearity of $\alpha _{\text {p}}$ = −0.81 and $\alpha _{\text {d}}$ = −0.31, near ideal asymmetry of 0.5, and free of read disturb degradation. High neural network accuracy (>87%) is also obtained.