{"title":"Trivalent Ionic Molecular Bridges as Efficient Charge-Trapping Method for All-Solid-State Organic Synaptic Transistors toward Neuromorphic Signal Processing Applications.","authors":"Taehoon Kim, Woongki Lee, Youngkyoo Kim","doi":"10.1002/smtd.202401885","DOIUrl":null,"url":null,"abstract":"<p><p>Achieving high retention of memory state is crucial in artificial synapse devices for neuromorphic computing systems. Of various memorizing methods, a charge-trapping method provides fast response times when it comes to the smallest size of electrons. Here, for the first time, it is demonstrated that trivalent molecular bridges with three ionic bond sites in the polymeric films can efficiently trap electrons in the organic synaptic transistors (OSTRs). A water-soluble polymer with sulfonic acid groups, poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA), is reacted with melamine (ML) to make trivalent molecular bridges with three ionic bond sites for the application of charge-trapping and gate-insulating layer in all-solid-state OSTRs. The OSTRs with the PAMPSA:ML layers are operated at low voltages (≤5 V) with pronounced hysteresis and high memory retention characteristics (ML = 25 mol%) and delivered excellent potentiation/depression performances under modulation of gate pulse frequency. The optimized OSTRs could successfully process analog (Morse/Braile) signals to synaptic current datasets for recognition/prediction logics with an accuracy of >95%, supporting strong potential as all-solid-state synaptic devices for neuromorphic systems in artificial intelligence applications.</p>","PeriodicalId":229,"journal":{"name":"Small Methods","volume":" ","pages":"e2401885"},"PeriodicalIF":10.7000,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small Methods","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/smtd.202401885","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Achieving high retention of memory state is crucial in artificial synapse devices for neuromorphic computing systems. Of various memorizing methods, a charge-trapping method provides fast response times when it comes to the smallest size of electrons. Here, for the first time, it is demonstrated that trivalent molecular bridges with three ionic bond sites in the polymeric films can efficiently trap electrons in the organic synaptic transistors (OSTRs). A water-soluble polymer with sulfonic acid groups, poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA), is reacted with melamine (ML) to make trivalent molecular bridges with three ionic bond sites for the application of charge-trapping and gate-insulating layer in all-solid-state OSTRs. The OSTRs with the PAMPSA:ML layers are operated at low voltages (≤5 V) with pronounced hysteresis and high memory retention characteristics (ML = 25 mol%) and delivered excellent potentiation/depression performances under modulation of gate pulse frequency. The optimized OSTRs could successfully process analog (Morse/Braile) signals to synaptic current datasets for recognition/prediction logics with an accuracy of >95%, supporting strong potential as all-solid-state synaptic devices for neuromorphic systems in artificial intelligence applications.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.