Advanced Functional Materials · 2023 · 29 citations · 45 references
EngineeringOrganic ElectronicsResponsive PolymersLongest Retention TimeChemistryPolymersConducting PolymerChemical EngineeringElectronic DevicesOrganic ElectrochemistryLong Retention TimePolymer ChemistryMaterials ScienceElectrical EngineeringOrganic SemiconductorElectrochemistryOrganic MaterialsOrganic Charge-transfer CompoundHigh State RetentionElectronic MaterialsPolymer ScienceConjugated PolymerRetention Time
Abstract To achieve superior device performance such as low threshold voltage V th , high maximum on‐current I on,max , and long retention time in electrolyte‐gated organic synaptic transistors, efficient electrochemical doping and high state retention are essential. However, these characteristics generally show a trade‐off relationship. This work introduces an effective strategy to increase retention time while promoting efficient electrochemical doping. The approach involves blending two polymer semiconductors (PSCs) that have the same backbone but different types of side chains. Polymer synaptic transistors (PSTs) with the blend film showed the lowest V th , highest I on,max , longest retention time, and superior cyclic stability compared to PSTs that used films containing only one of the PSCs. The improvement in electrical and synaptic properties achieved through the blend strategy is consistently reproducible and comprehensive. It is attributed this improvement to the increased redox activity and constrained morphological changes observed in the blended PSCs during electrochemical doping, as confirmed by several electrochemical characterizations. This work is the first to increase retention time in PSTs without increasing the crystallinity of polymer film or sacrificing the electrochemical doping efficiency, which has been regarded as an unavoidable compromise in this field. This method provides an effective way to tune synaptic properties for various neuromorphic applications.
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A bioinspired flexible organic artificial afferent nerve
Yeongin Kim, Alex Chortos, Wentao Xu et al. · Science · 2018 · 1.4K citations
Organic electronics for neuromorphic computing
Yoeri van de Burgt, Armantas Melianas, Scott T. Keene et al. · Nature Electronics · 2018 · 1.1K citations
Geoffrey W. Burr, R. M. Shelby, Severin Sidler et al. · IEEE Transactions on Electron Devices · 2015 · 904 citations · Full text
Large-scale Neural Network, Engineering, Neural Networks (Machine Learning) +22