From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing
Moon‐Seok Kim, Sungho Kim
Abstract
This review presents a comparative analysis of the analog switching performance of oxide- and two-dimensional (2D)-material-based memristors, focusing on their application in neuromorphic computing systems. This study examines various performance metrics such as endurance, energy consumption, and switching characteristics to elucidate how these parameters are influenced by the unique characteristics of the respective materials. By examining both oxide- and 2D-material-based memristors in array configurations, this review provides insights into their suitability for neuromorphic computing, highlighting advancements, challenges, and future research directions in memristor technology.
Topics & Concepts
Neuromorphic engineeringMemristorComputer scienceComputer architectureEnergy consumptionEfficient energy useEnergy (signal processing)Materials scienceNanotechnologyElectronic engineeringArtificial neural networkArtificial intelligenceEngineeringElectrical engineeringPhysicsQuantum mechanicsAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesTransition Metal Oxide Nanomaterials