Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices
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 de...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400481 |
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| author | Henrique Frulani de Paula Barbosa Andreas Schander Andika Asyuda Luka Bislich Sarah Bornemann Björn Lüssem |
| author_facet | Henrique Frulani de Paula Barbosa Andreas Schander Andika Asyuda Luka Bislich Sarah Bornemann Björn Lüssem |
| author_sort | Henrique Frulani de Paula Barbosa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5caea4b3bdc4db6989a4775b004a2b3 |
| institution | Kabale University |
| issn | 2199-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-b5caea4b3bdc4db6989a4775b004a2b32025-01-09T11:51:13ZengWiley-VCHAdvanced Electronic Materials2199-160X2024-12-011012n/an/a10.1002/aelm.202400481Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic DevicesHenrique Frulani de Paula Barbosa0Andreas Schander1Andika Asyuda2Luka Bislich3Sarah Bornemann4Björn Lüssem5Institut für Mikrosensoren ‐Aktoren, und ‐Systeme (IMSAS) Universität Bremen Otto‐Hahn‐Allee 1 28359 Bremen GermanyInstitut für Mikrosensoren ‐Aktoren, und ‐Systeme (IMSAS) Universität Bremen Otto‐Hahn‐Allee 1 28359 Bremen GermanyInstitut für Mikrosensoren ‐Aktoren, und ‐Systeme (IMSAS) Universität Bremen Otto‐Hahn‐Allee 1 28359 Bremen GermanyInstitut für Mikrosensoren ‐Aktoren, und ‐Systeme (IMSAS) Universität Bremen Otto‐Hahn‐Allee 1 28359 Bremen GermanyInstitut für Mikrosensoren ‐Aktoren, und ‐Systeme (IMSAS) Universität Bremen Otto‐Hahn‐Allee 1 28359 Bremen GermanyInstitut für Mikrosensoren ‐Aktoren, und ‐Systeme (IMSAS) Universität Bremen Otto‐Hahn‐Allee 1 28359 Bremen GermanyAbstract 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.https://doi.org/10.1002/aelm.202400481ENODememoryneuromorphicsnon‐volatileOECT |
| spellingShingle | Henrique Frulani de Paula Barbosa Andreas Schander Andika Asyuda Luka Bislich Sarah Bornemann Björn Lüssem Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices Advanced Electronic Materials ENODe memory neuromorphics non‐volatile OECT |
| title | Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices |
| title_full | Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices |
| title_fullStr | Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices |
| title_full_unstemmed | Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices |
| title_short | Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices |
| title_sort | role of trapping in non volatility of electrochemical neuromorphic organic devices |
| topic | ENODe memory neuromorphics non‐volatile OECT |
| url | https://doi.org/10.1002/aelm.202400481 |
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