Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design
Abstract There is increasing interest in using specialized circuits based on emerging technologies to develop a new generation of smart devices. The process and device variability exhibited by such materials, however, can present substantial challenges for designing circuits. Three models are consid...
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| Main Authors: | , , , , , , , , , |
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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.202400515 |
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| _version_ | 1841553183551258624 |
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| author | Laurie E. Calvet Sami El‐Nakouzi Zonglong Li Yerin Kim Amer Zaibi Patryk Golec Ie Mei Bhattacharyya Yvan Bonnassieux Lina Kadura Benjamin Iñiguez |
| author_facet | Laurie E. Calvet Sami El‐Nakouzi Zonglong Li Yerin Kim Amer Zaibi Patryk Golec Ie Mei Bhattacharyya Yvan Bonnassieux Lina Kadura Benjamin Iñiguez |
| author_sort | Laurie E. Calvet |
| collection | DOAJ |
| description | Abstract There is increasing interest in using specialized circuits based on emerging technologies to develop a new generation of smart devices. The process and device variability exhibited by such materials, however, can present substantial challenges for designing circuits. Three models are considered here: a physical compact model, an empirical look‐up table, and an empirical surrogate model based on a multilayer perceptron (MLP) regression. Each one is fit to measurements of discrete organic thin film transistors in the low voltage regime. It is shown that the models provide consistent results when designing artificial neuron circuits, but that the MLP regression provides the highest accuracy and is much simpler to fit compared to the compact model. The targeted technology exhibits non‐ideal behavior such as variable threshold voltage and hysteresis. Using the MLP regression model, the effect of such variability on the performance of an artificial neuron circuit is compared. It is found that these effects alter the neuron firing rate and change the time spent in the on/off states but do not change the basic operation. |
| format | Article |
| id | doaj-art-d241e365557f4c71a5d786d78854b2e7 |
| 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-d241e365557f4c71a5d786d78854b2e72025-01-09T11:51:13ZengWiley-VCHAdvanced Electronic Materials2199-160X2024-12-011012n/an/a10.1002/aelm.202400515Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit DesignLaurie E. Calvet0Sami El‐Nakouzi1Zonglong Li2Yerin Kim3Amer Zaibi4Patryk Golec5Ie Mei Bhattacharyya6Yvan Bonnassieux7Lina Kadura8Benjamin Iñiguez9LPICM CNRS, Ecole Polytechnique, Institute Polytechnique de Paris Palaiseau 91128 FranceLPICM CNRS, Ecole Polytechnique, Institute Polytechnique de Paris Palaiseau 91128 FranceLPICM CNRS, Ecole Polytechnique, Institute Polytechnique de Paris Palaiseau 91128 FranceLPICM CNRS, Ecole Polytechnique, Institute Polytechnique de Paris Palaiseau 91128 FranceDEERA University Rovira i Virgili Tarragona 43007 SpainAdvanced Technology Institute School of Computer Science and Electronic Engineering University of Surrey Guildford GU2 7XH UKLPICM CNRS, Ecole Polytechnique, Institute Polytechnique de Paris Palaiseau 91128 FranceLPICM CNRS, Ecole Polytechnique, Institute Polytechnique de Paris Palaiseau 91128 FranceCEA‐LITEN Université Grenoble‐Alpes Grenoble 38000 FranceDEERA University Rovira i Virgili Tarragona 43007 SpainAbstract There is increasing interest in using specialized circuits based on emerging technologies to develop a new generation of smart devices. The process and device variability exhibited by such materials, however, can present substantial challenges for designing circuits. Three models are considered here: a physical compact model, an empirical look‐up table, and an empirical surrogate model based on a multilayer perceptron (MLP) regression. Each one is fit to measurements of discrete organic thin film transistors in the low voltage regime. It is shown that the models provide consistent results when designing artificial neuron circuits, but that the MLP regression provides the highest accuracy and is much simpler to fit compared to the compact model. The targeted technology exhibits non‐ideal behavior such as variable threshold voltage and hysteresis. Using the MLP regression model, the effect of such variability on the performance of an artificial neuron circuit is compared. It is found that these effects alter the neuron firing rate and change the time spent in the on/off states but do not change the basic operation.https://doi.org/10.1002/aelm.202400515artificial neuron circuitscircuit simulationsdevice modellingorganic electronics |
| spellingShingle | Laurie E. Calvet Sami El‐Nakouzi Zonglong Li Yerin Kim Amer Zaibi Patryk Golec Ie Mei Bhattacharyya Yvan Bonnassieux Lina Kadura Benjamin Iñiguez Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design Advanced Electronic Materials artificial neuron circuits circuit simulations device modelling organic electronics |
| title | Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design |
| title_full | Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design |
| title_fullStr | Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design |
| title_full_unstemmed | Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design |
| title_short | Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design |
| title_sort | simulating organic thin film transistors using multilayer perceptron regression models to enable circuit design |
| topic | artificial neuron circuits circuit simulations device modelling organic electronics |
| url | https://doi.org/10.1002/aelm.202400515 |
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