A study on MoS2-based multilevel transistor memories for neuromorphic computing
Da Li, Byunghoon Ryu, Xiaogan Liang
Abstract
We study the validity of implementing MoS2 multilevel memories in future neuromorphic networks. Such a validity is determined by the number of available states per memory and their retention characteristics within the nominal computing duration. Our work shows that MoS2 memories have at least 3-bit and 4.7-bit resolvable states suitable for hour-scale and minute-scale computing processes, respectively. The simulated neural network conceptually constructed on the basis of such memory states predicts a high learning accuracy of 90.9% for handwritten digit datasets. This work indicates that multilevel MoS2 transistors could be exploited as valid and reliable nodes for constructing neuromorphic networks.
Topics & Concepts
Neuromorphic engineeringComputer scienceArtificial neural networkScale (ratio)TransistorReservoir computingComputer architectureParallel computingArtificial intelligenceRecurrent neural networkElectrical engineeringPhysicsEngineeringVoltageQuantum mechanicsAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Reservoir Computing