In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor
State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switchi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/14/18/5223 |
| _version_ | 1797518383631040512 |
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| author | Nikolaos Vasileiadis Vasileios Ntinas Georgios Ch. Sirakoulis Panagiotis Dimitrakis |
| author_facet | Nikolaos Vasileiadis Vasileios Ntinas Georgios Ch. Sirakoulis Panagiotis Dimitrakis |
| author_sort | Nikolaos Vasileiadis |
| collection | DOAJ |
| description | State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiN<sub>x</sub>) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed. |
| first_indexed | 2024-03-10T07:29:07Z |
| format | Article |
| id | doaj.art-954161860a9c463d886f09ac4b58be5f |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T07:29:07Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-954161860a9c463d886f09ac4b58be5f2023-11-22T14:00:44ZengMDPI AGMaterials1996-19442021-09-011418522310.3390/ma14185223In-Memory-Computing Realization with a Photodiode/Memristor Based Vision SensorNikolaos Vasileiadis0Vasileios Ntinas1Georgios Ch. Sirakoulis2Panagiotis Dimitrakis3Institute of Nanoscience and Nanotechnology, National Center for Scientific Research “Demokritos”, 15341 Agia Paraskevi, GreeceDepartment of Electrical and Computer Engineering, Democritus University of Thrace (DUTh), 67100 Xanthi, GreeceDepartment of Electrical and Computer Engineering, Democritus University of Thrace (DUTh), 67100 Xanthi, GreeceInstitute of Nanoscience and Nanotechnology, National Center for Scientific Research “Demokritos”, 15341 Agia Paraskevi, GreeceState-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiN<sub>x</sub>) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed.https://www.mdpi.com/1996-1944/14/18/5223resistive random-access memory (RRAM)resistance switchingsilicon nitridememristorvision sensorphotodiode |
| spellingShingle | Nikolaos Vasileiadis Vasileios Ntinas Georgios Ch. Sirakoulis Panagiotis Dimitrakis In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor Materials resistive random-access memory (RRAM) resistance switching silicon nitride memristor vision sensor photodiode |
| title | In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor |
| title_full | In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor |
| title_fullStr | In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor |
| title_full_unstemmed | In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor |
| title_short | In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor |
| title_sort | in memory computing realization with a photodiode memristor based vision sensor |
| topic | resistive random-access memory (RRAM) resistance switching silicon nitride memristor vision sensor photodiode |
| url | https://www.mdpi.com/1996-1944/14/18/5223 |
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