Proposal and Experimental Demonstration of Reservoir Computing using Hf0.5Zr0.5O2/Si FeFETs for Neuromorphic Applications
Eishin Nako, Kasidit Toprasertpong, Ryosho Nakane, Z. Wang, Yuto Miyatake, Mitsuru Takenaka, Shun Takagi
Abstract
We propose a new AI calculation scheme by reservoir computing utilizing the memory effect and non-linearity of ferroelectric gate MOSFETs (FeFETs) for neuromorphic applications. The task operations of time-series data are experimentally demonstrated by taking time responses of the drain current for gate voltage input as the virtual nodes. A high ability to classify input data is experimentally verified.
Topics & Concepts
Neuromorphic engineeringComputer scienceReservoir computingTask (project management)Scheme (mathematics)LinearityElectronic engineeringVoltageLogic gateComputer architectureMaterials scienceElectrical engineeringEngineeringArtificial intelligenceAlgorithmArtificial neural networkMathematicsSystems engineeringRecurrent neural networkMathematical analysisAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Reservoir Computing