Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications
The intriguing properties of two-dimensional (2D) materials render them attractive for energy efficient neuromorphic computations because their atomic scale thickness can alleviate the power requirements of the device. In parallel, their layered structure can be leveraged to optically program the de...
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IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
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| Online Access: | https://doi.org/10.1088/2634-4386/add36d |
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| author | Chrysi Panagopoulou Panagiotis Bousoulas Georgia Iliani Papouli Maria Kainourgiaki Charalampos Tsioustas Maria Karnachoriti Athanassios G Kontos Dimitris Tsoukalas |
| author_facet | Chrysi Panagopoulou Panagiotis Bousoulas Georgia Iliani Papouli Maria Kainourgiaki Charalampos Tsioustas Maria Karnachoriti Athanassios G Kontos Dimitris Tsoukalas |
| author_sort | Chrysi Panagopoulou |
| collection | DOAJ |
| description | The intriguing properties of two-dimensional (2D) materials render them attractive for energy efficient neuromorphic computations because their atomic scale thickness can alleviate the power requirements of the device. In parallel, their layered structure can be leveraged to optically program the device and further reduce power consumption. Along these lines, in this work, a forming free memory device consisting of a MoS _2 monolayer with dimensions of ∼100 μ m decorated with small (∼3 nm diameter) Pt nanoparticles (NPs) was fabricated. The impact of the Pt NPs’ surface density on the optoelectronic neuromorphic properties under ultraviolet irradiation ( λ = 390 nm) was systematically investigated. More specifically, the reference samples without Pt NPs exhibited only synaptic behavior, while the NPs-based one (surface density of ∼2 × 10 ^12 NPs cm ^−2 ) presented a neuron-like response. An elevated surface density (∼5 × 10 ^12 NPs cm ^−2 ) just reduced the frequency of the generated spikes. Various synaptic plasticity and neuronal coding schemes were experimentally demonstrated. The underlying origins of this behavior were attributed to band bending in the Pt NPs-MoS _2 interface, leading to a trapping effect of electrons on the metallic NPs, evidenced by photoluminescence quenching, followed by a detrapping process, demonstrated by the reduced firing rate when using the Pt layer with the higher surface density. This versatility of the devices was leveraged to simulate the behavior of a fully optoelectronic spiking neural network. Considering the low energy consumption per spike (∼400 pJ) during the experimental recorded neuromorphic properties, a dramatically reduced power consumption of ∼320 μ W was extracted during the pattern recognition of optical images. Our work provides valuable insights for emulating the artificial synaptic and neuronal behavior and paves the way for the development of next-generation and fully memristive artificial neural networks with a very small energy footprint. |
| format | Article |
| id | doaj-art-5b7181a3bdf849219d15f47f3dc5ded2 |
| institution | DOAJ |
| issn | 2634-4386 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-5b7181a3bdf849219d15f47f3dc5ded22025-08-20T02:58:43ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862025-01-015202401010.1088/2634-4386/add36dReconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applicationsChrysi Panagopoulou0https://orcid.org/0009-0004-9304-0614Panagiotis Bousoulas1https://orcid.org/0000-0002-5395-0777Georgia Iliani Papouli2https://orcid.org/0009-0000-8296-402XMaria Kainourgiaki3Charalampos Tsioustas4Maria Karnachoriti5https://orcid.org/0000-0001-6227-3272Athanassios G Kontos6Dimitris Tsoukalas7https://orcid.org/0000-0001-5189-3396Department of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceDepartment of Applied Physics, National Technical University of Athens , 15780 Zografou, GreeceThe intriguing properties of two-dimensional (2D) materials render them attractive for energy efficient neuromorphic computations because their atomic scale thickness can alleviate the power requirements of the device. In parallel, their layered structure can be leveraged to optically program the device and further reduce power consumption. Along these lines, in this work, a forming free memory device consisting of a MoS _2 monolayer with dimensions of ∼100 μ m decorated with small (∼3 nm diameter) Pt nanoparticles (NPs) was fabricated. The impact of the Pt NPs’ surface density on the optoelectronic neuromorphic properties under ultraviolet irradiation ( λ = 390 nm) was systematically investigated. More specifically, the reference samples without Pt NPs exhibited only synaptic behavior, while the NPs-based one (surface density of ∼2 × 10 ^12 NPs cm ^−2 ) presented a neuron-like response. An elevated surface density (∼5 × 10 ^12 NPs cm ^−2 ) just reduced the frequency of the generated spikes. Various synaptic plasticity and neuronal coding schemes were experimentally demonstrated. The underlying origins of this behavior were attributed to band bending in the Pt NPs-MoS _2 interface, leading to a trapping effect of electrons on the metallic NPs, evidenced by photoluminescence quenching, followed by a detrapping process, demonstrated by the reduced firing rate when using the Pt layer with the higher surface density. This versatility of the devices was leveraged to simulate the behavior of a fully optoelectronic spiking neural network. Considering the low energy consumption per spike (∼400 pJ) during the experimental recorded neuromorphic properties, a dramatically reduced power consumption of ∼320 μ W was extracted during the pattern recognition of optical images. Our work provides valuable insights for emulating the artificial synaptic and neuronal behavior and paves the way for the development of next-generation and fully memristive artificial neural networks with a very small energy footprint.https://doi.org/10.1088/2634-4386/add36dOptoelectronic propertiesmemristorscharge trappingnanoparticlesneuronsynapse |
| spellingShingle | Chrysi Panagopoulou Panagiotis Bousoulas Georgia Iliani Papouli Maria Kainourgiaki Charalampos Tsioustas Maria Karnachoriti Athanassios G Kontos Dimitris Tsoukalas Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications Neuromorphic Computing and Engineering Optoelectronic properties memristors charge trapping nanoparticles neuron synapse |
| title | Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications |
| title_full | Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications |
| title_fullStr | Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications |
| title_full_unstemmed | Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications |
| title_short | Reconfigurable optoelectronic neuromorphic properties of MoS2-based memristors decorated with Pt nanoparticles for low power spiking neural network applications |
| title_sort | reconfigurable optoelectronic neuromorphic properties of mos2 based memristors decorated with pt nanoparticles for low power spiking neural network applications |
| topic | Optoelectronic properties memristors charge trapping nanoparticles neuron synapse |
| url | https://doi.org/10.1088/2634-4386/add36d |
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