Henrique Frulani de Paula Barbosa, Andreas Schander, Andika Asyuda, Luka Bislich, Sarah Bornemann, Björn Lüssem
{"title":"Role of Trapping in Non-Volatility of Electrochemical Neuromorphic Organic Devices","authors":"Henrique Frulani de Paula Barbosa, Andreas Schander, Andika Asyuda, Luka Bislich, Sarah Bornemann, Björn Lüssem","doi":"10.1002/aelm.202400481","DOIUrl":null,"url":null,"abstract":"<p>Artificial Neural Networks (ANN) require a better platform to reduce their energy consumption and achieve their full potential. Electrochemical devices like the Electrochemical Neuromorphic Organic Device (ENODe) stand out as a potential building block for ANNs, due to their lower energy demand, in addition to their biocompatibility and access to multiple and stable memory levels. However, the non-volatile effect observed in these devices is not yet fully understood. Hence, here we propose a 2D drift-diffusion model that is capable to reproduce the device behavior. The model relies on the assumption of trapping sites for cations, which are increasingly filled or emptied during subsequent pre-synaptic pulses. The model is verified by experiments on devices with varying post-synaptic dimensions. Overall, the results provide a framework to discuss ENODe operation and design strategies for ENODes with well-controlled memory states.</p>","PeriodicalId":110,"journal":{"name":"Advanced Electronic Materials","volume":"10 12","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aelm.202400481","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Electronic Materials","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aelm.202400481","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial Neural Networks (ANN) require a better platform to reduce their energy consumption and achieve their full potential. Electrochemical devices like the Electrochemical Neuromorphic Organic Device (ENODe) stand out as a potential building block for ANNs, due to their lower energy demand, in addition to their biocompatibility and access to multiple and stable memory levels. However, the non-volatile effect observed in these devices is not yet fully understood. Hence, here we propose a 2D drift-diffusion model that is capable to reproduce the device behavior. The model relies on the assumption of trapping sites for cations, which are increasingly filled or emptied during subsequent pre-synaptic pulses. The model is verified by experiments on devices with varying post-synaptic dimensions. Overall, the results provide a framework to discuss ENODe operation and design strategies for ENODes with well-controlled memory states.
期刊介绍:
Advanced Electronic Materials is an interdisciplinary forum for peer-reviewed, high-quality, high-impact research in the fields of materials science, physics, and engineering of electronic and magnetic materials. It includes research on physics and physical properties of electronic and magnetic materials, spintronics, electronics, device physics and engineering, micro- and nano-electromechanical systems, and organic electronics, in addition to fundamental research.