CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing
Abstract Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-11-01
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| Series: | Nanoscale Research Letters |
| Online Access: | https://doi.org/10.1186/s11671-022-03744-x |
| _version_ | 1827881663961497600 |
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| author | Facai Wu Chien-Hung Chou Tseung-Yuen Tseng |
| author_facet | Facai Wu Chien-Hung Chou Tseung-Yuen Tseng |
| author_sort | Facai Wu |
| collection | DOAJ |
| description | Abstract Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 104 s, and endurance of 106 cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application. |
| first_indexed | 2024-03-12T18:44:34Z |
| format | Article |
| id | doaj.art-43e85415ef374685a377bb098f1a1a16 |
| institution | Directory Open Access Journal |
| issn | 1556-276X |
| language | English |
| last_indexed | 2024-03-12T18:44:34Z |
| publishDate | 2022-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Nanoscale Research Letters |
| spelling | doaj.art-43e85415ef374685a377bb098f1a1a162023-08-02T07:45:48ZengSpringerOpenNanoscale Research Letters1556-276X2022-11-011711910.1186/s11671-022-03744-xCMOS-Compatible Memristor for Optoelectronic Neuromorphic ComputingFacai Wu0Chien-Hung Chou1Tseung-Yuen Tseng2Institute of Electronics, National Yang Ming Chiao Tung UniversityInstitute of Electronics, National Yang Ming Chiao Tung UniversityInstitute of Electronics, National Yang Ming Chiao Tung UniversityAbstract Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 104 s, and endurance of 106 cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.https://doi.org/10.1186/s11671-022-03744-x |
| spellingShingle | Facai Wu Chien-Hung Chou Tseung-Yuen Tseng CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing Nanoscale Research Letters |
| title | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
| title_full | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
| title_fullStr | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
| title_full_unstemmed | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
| title_short | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
| title_sort | cmos compatible memristor for optoelectronic neuromorphic computing |
| url | https://doi.org/10.1186/s11671-022-03744-x |
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