{"title":"Speckle Pattern Analysis of PVK:rGO Composite Based Memristor Device","authors":"Ramin Jamali, Madeh Sajjadi, Babak Taherkhani, Davood Abbaszadeh, Ali‐Reza Moradi","doi":"10.1002/mame.202400213","DOIUrl":null,"url":null,"abstract":"The memristors are expected to be fundamental devices for neuromorphic systems and switching applications. The device made of a sandwiched layer of poly(N‐ vinylcarbazole) and reduced graphene composite between asymmetric electrodes (ITO/PVK:rGO/Al) exhibits bistable resistive switching behavior. The performance of the memristor can be optimized by controlling the doped graphene oxide. To assess the device performance when it switches between ON and OFF states, optical characterization approaches are highly promising due to their non‐destructive and remote nature. Here, speckle pattern (SP) analysis to this end is introduced. SPs include a huge amount of information about their generating mechanism, which is extracted through statistical elaboration. SPs of the PVK:rGO in different states in situ and examine the conduction mechanism is acquired. The variations in the statistical parameters are attributed to the resistance state of the PVK:rGO with regard to the physical switching mechanism. The resistance/conduction state, in turn, depends on the activity and properties of PVK:rGO memristors, as well as the additional non‐uniformities induced through the variations of density of carriers. The present optical methodology can be potentially served as a bench‐top device for characterization purposes of similar devices during their operating.","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/mame.202400213","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The memristors are expected to be fundamental devices for neuromorphic systems and switching applications. The device made of a sandwiched layer of poly(N‐ vinylcarbazole) and reduced graphene composite between asymmetric electrodes (ITO/PVK:rGO/Al) exhibits bistable resistive switching behavior. The performance of the memristor can be optimized by controlling the doped graphene oxide. To assess the device performance when it switches between ON and OFF states, optical characterization approaches are highly promising due to their non‐destructive and remote nature. Here, speckle pattern (SP) analysis to this end is introduced. SPs include a huge amount of information about their generating mechanism, which is extracted through statistical elaboration. SPs of the PVK:rGO in different states in situ and examine the conduction mechanism is acquired. The variations in the statistical parameters are attributed to the resistance state of the PVK:rGO with regard to the physical switching mechanism. The resistance/conduction state, in turn, depends on the activity and properties of PVK:rGO memristors, as well as the additional non‐uniformities induced through the variations of density of carriers. The present optical methodology can be potentially served as a bench‐top device for characterization purposes of similar devices during their operating.
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.