CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914272875905024 |
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| author | Patra, Umakanta Sikdar, Subhrajit Padhan, Roshan Kaur, Amandeep Sahoo, Satyaprakash Dhar, Subhabrata |
| author_facet | Patra, Umakanta Sikdar, Subhrajit Padhan, Roshan Kaur, Amandeep Sahoo, Satyaprakash Dhar, Subhabrata |
| contents | Demand for lower computing power has rapidly increased. In this context, brain-inspired neuromorphic computing, which integrate data storage and processing, has attracted significant attention. Here, our study reveals that field effect transistors fabricated on chemical vapor deposited bilayer (2L) MoS2 films can mimic the functions of biological synapse. These devices demonstrate high level of pair pulse facilitation (PPF), short term to long term memory (STM-to-LTM) transition as well as learning-forgetting-relearning properties. Effect of light intensity, pulse number, pulse width and photon energy on the STM-to-LTM transition is studied. It has been found that the rate of depression of the memory state can be controlled using the gate bias. Electrical and optical energy consumptions per synaptic event are estimated to be as low as 280 fJ and 20 nJ, respectively. Furthermore, photocurrent in these devices is observed to increase linearly with the number of the excitation pulses. This property has been exploited to demonstrate different arithmetic operations by the device. Moreover, these devices show great potential for image recognition. Artificial neural network simulation has returned an image recognition accuracy of ~85%. All these findings show a great prospect of 2L-MoS2 for developing low power, transparent and flexible neuromorphic devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15917 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications Patra, Umakanta Sikdar, Subhrajit Padhan, Roshan Kaur, Amandeep Sahoo, Satyaprakash Dhar, Subhabrata Materials Science Demand for lower computing power has rapidly increased. In this context, brain-inspired neuromorphic computing, which integrate data storage and processing, has attracted significant attention. Here, our study reveals that field effect transistors fabricated on chemical vapor deposited bilayer (2L) MoS2 films can mimic the functions of biological synapse. These devices demonstrate high level of pair pulse facilitation (PPF), short term to long term memory (STM-to-LTM) transition as well as learning-forgetting-relearning properties. Effect of light intensity, pulse number, pulse width and photon energy on the STM-to-LTM transition is studied. It has been found that the rate of depression of the memory state can be controlled using the gate bias. Electrical and optical energy consumptions per synaptic event are estimated to be as low as 280 fJ and 20 nJ, respectively. Furthermore, photocurrent in these devices is observed to increase linearly with the number of the excitation pulses. This property has been exploited to demonstrate different arithmetic operations by the device. Moreover, these devices show great potential for image recognition. Artificial neural network simulation has returned an image recognition accuracy of ~85%. All these findings show a great prospect of 2L-MoS2 for developing low power, transparent and flexible neuromorphic devices. |
| title | CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications |
| topic | Materials Science |
| url | https://arxiv.org/abs/2601.15917 |