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Back‐End‐of‐Line SiC‐Based Memristor for Resistive Memory and Artificial Synapse

Omesh Kapur, Dongkai Guo, Jamie D. Reynolds, Yisong Han, Richard Beanland, Liudi Jiang, C.H. de Groot, Ruomeng Huang

2022Advanced Electronic Materials48 citationsDOIOpen Access PDF

Abstract

Abstract Two‐terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back‐end‐of‐line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance‐free and self‐rectifying characteristics which are advantageous for the implementation of high‐density 3D crossbar memory architectures. The conductance of this SiC‐based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired‐pulse facilitation (PPF), post‐tetanic potentiation (PTP), short‐term potentiation (STP), and spike‐rate‐dependent plasticity (SRDP). The synaptic function of learning‐forgetting‐relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC‐based memristor and its application in both memory and neuromorphic computing.

Topics & Concepts

Neuromorphic engineeringMemristorMaterials scienceSynapseLong-term potentiationCrossbar switchResistive random-access memorySynaptic weightNeural facilitationForgettingComputer scienceOptoelectronicsNanotechnologyVoltageNeuroscienceElectrical engineeringArtificial intelligenceArtificial neural networkEngineeringTelecommunicationsChemistryBiochemistryLinguisticsBiologyPhilosophyReceptorAdvanced Memory and Neural ComputingNeuroscience and Neural EngineeringPhotoreceptor and optogenetics research
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