Remaining useful life prediction towards cycling stability of organic electrochemical transistors
Organic electrochemical transistors (OECTs) show abundant potential in biosensors, artificial neuromorphic systems, brain-machine interfaces, etc With the fast development of novel functional materials and new device structures, OECTs with high transconductance (g _m > mS) and good cycling stabil...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Materials Research Express |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2053-1591/ad20a7 |
| _version_ | 1797338003528482816 |
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| author | Jie Xu Miao Xie Xinhao Wu Kunshu Xiao Yaoyu Ding Libing Bai Cheng-Geng Huang Wei Huang |
| author_facet | Jie Xu Miao Xie Xinhao Wu Kunshu Xiao Yaoyu Ding Libing Bai Cheng-Geng Huang Wei Huang |
| author_sort | Jie Xu |
| collection | DOAJ |
| description | Organic electrochemical transistors (OECTs) show abundant potential in biosensors, artificial neuromorphic systems, brain-machine interfaces, etc With the fast development of novel functional materials and new device structures, OECTs with high transconductance (g _m > mS) and good cycling stabilities (> 10,000 cycles) have been developed. While stability characterization is always time-consuming, to accelerate the development and commercialization of OECTs, tools for stability prediction are urgently needed. In this paper, OECTs with good cycling stabilities are realized by minimizing the gate voltage amplitude during cycling, while a remaining useful life (RUL) prediction framework for OECTs is proposed. Specifically, OECTs based on p(g2T-T) show tremendously enhanced stability which exhibits only 46.1% on-current (I _ON ) and 33.2% peak g _m decreases after 80,000 cycles (53 min). Then, RUL prediction is proposed based on the run-to-failure (RtF) aging tests (cycling stability test of OECTs). By selecting two aging parameters (I _ON and peak g _m ) as health indicators (HI), a novel multi-scale feature fusion (MFF) method for RUL prediction is proposed, which consists of a long short-term memory (LSTM) neural network based multi-scale feature generator (MFG) module for feature extraction and an attention-based feature fusion (AFF) module for feature fusion. Consequently, richer effective information is utilized to improve the prediction performance, where the experimental results show the superiority of the proposed framework on multiple OECTs in RUL prediction tasks. Therefore, by introducing such a powerful framework for the evaluation of the lifetime of OECTs, further optimization of materials, devices, and integrated systems relevant to OECTs will be stimulated. Moreover, this tool can also be extended to other relevant bioelectronics. |
| first_indexed | 2024-03-08T09:24:48Z |
| format | Article |
| id | doaj.art-3ab8ced7211149428fb964889706bdaa |
| institution | Directory Open Access Journal |
| issn | 2053-1591 |
| language | English |
| last_indexed | 2024-03-08T09:24:48Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj.art-3ab8ced7211149428fb964889706bdaa2024-01-31T10:28:10ZengIOP PublishingMaterials Research Express2053-15912024-01-0111101510110.1088/2053-1591/ad20a7Remaining useful life prediction towards cycling stability of organic electrochemical transistorsJie Xu0Miao Xie1Xinhao Wu2Kunshu Xiao3Yaoyu Ding4Libing Bai5Cheng-Geng Huang6Wei Huang7https://orcid.org/0000-0002-0973-8015School of Automation Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China , Chengdu, People’s Republic of ChinaOrganic electrochemical transistors (OECTs) show abundant potential in biosensors, artificial neuromorphic systems, brain-machine interfaces, etc With the fast development of novel functional materials and new device structures, OECTs with high transconductance (g _m > mS) and good cycling stabilities (> 10,000 cycles) have been developed. While stability characterization is always time-consuming, to accelerate the development and commercialization of OECTs, tools for stability prediction are urgently needed. In this paper, OECTs with good cycling stabilities are realized by minimizing the gate voltage amplitude during cycling, while a remaining useful life (RUL) prediction framework for OECTs is proposed. Specifically, OECTs based on p(g2T-T) show tremendously enhanced stability which exhibits only 46.1% on-current (I _ON ) and 33.2% peak g _m decreases after 80,000 cycles (53 min). Then, RUL prediction is proposed based on the run-to-failure (RtF) aging tests (cycling stability test of OECTs). By selecting two aging parameters (I _ON and peak g _m ) as health indicators (HI), a novel multi-scale feature fusion (MFF) method for RUL prediction is proposed, which consists of a long short-term memory (LSTM) neural network based multi-scale feature generator (MFG) module for feature extraction and an attention-based feature fusion (AFF) module for feature fusion. Consequently, richer effective information is utilized to improve the prediction performance, where the experimental results show the superiority of the proposed framework on multiple OECTs in RUL prediction tasks. Therefore, by introducing such a powerful framework for the evaluation of the lifetime of OECTs, further optimization of materials, devices, and integrated systems relevant to OECTs will be stimulated. Moreover, this tool can also be extended to other relevant bioelectronics.https://doi.org/10.1088/2053-1591/ad20a7organic electrochemical transistorRUL predictioncycling aging testdata-driven methodology |
| spellingShingle | Jie Xu Miao Xie Xinhao Wu Kunshu Xiao Yaoyu Ding Libing Bai Cheng-Geng Huang Wei Huang Remaining useful life prediction towards cycling stability of organic electrochemical transistors Materials Research Express organic electrochemical transistor RUL prediction cycling aging test data-driven methodology |
| title | Remaining useful life prediction towards cycling stability of organic electrochemical transistors |
| title_full | Remaining useful life prediction towards cycling stability of organic electrochemical transistors |
| title_fullStr | Remaining useful life prediction towards cycling stability of organic electrochemical transistors |
| title_full_unstemmed | Remaining useful life prediction towards cycling stability of organic electrochemical transistors |
| title_short | Remaining useful life prediction towards cycling stability of organic electrochemical transistors |
| title_sort | remaining useful life prediction towards cycling stability of organic electrochemical transistors |
| topic | organic electrochemical transistor RUL prediction cycling aging test data-driven methodology |
| url | https://doi.org/10.1088/2053-1591/ad20a7 |
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