| Type of Material: | Thesis |
| Title: | Switching parameter extraction of binary oxide based memristive synaptic devices |
| Researcher: | Patnaik, Asutosh |
| Guide: | Sahoo, Narayan | Panda, Debashis |
| Department: | Department of Electronic Sciences |
| Publisher: | Berhampur University, Berhampur |
| Place: | Berhampur |
| Year: | 2024 |
| Language: | English |
| Subject: | Computer Science | Computer Science Information Systems | Engineering and Technology | Electronic Science | Physical and Basic Sciences |
| Dissertation/Thesis Note: | PhD; Department of Electronic Sciences, Berhampur University, Berhampur, Berhampur; 2024 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-15 14:53:23 | |
| 008 | __ | 261015t2024||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464235 |
| 040 | __ | |dIN-AhILN|aBRMP_760007 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aPatnaik, Asutosh |
| 110 | __ | |aDepartment of Electronic Sciences|0U-0350|dBerhampur|bBerhampur University, Berhampur|ein |
| 245 | __ | |aSwitching parameter extraction of binary oxide based memristive synaptic devices |
| 260 | __ | |aBerhampur|c2024|bBerhampur University, Berhampur |
| 300 | __ | |a106p.|dCD |
| 502 | __ | |cDepartment of Electronic Sciences, Berhampur University, Berhampur, Berhampur|d2024|bPhD |
| 518 | __ | |oDate of Award|d2025 |
| 518 | __ | |d2017|oDate of Registration |
| 518 | __ | |d2024|oDate of Completion |
| 520 | __ | |aModern digital computer systems are primarily built around the von-Neumann architecture, the foundational model for computing systems for many decades. However, this architecture is encountering significant limitations, such as higher performance, greater energy efficiency, and adaptability in processing. As computational demands grow, especially with the rise of big data, artificial intelligence (AI), and complex simulations, alternative computing systems are being explored to overcome the limitations of traditional von-Neumann systems. As an alternative, neuromorphic computing is designed to address the critical bottlenecks of conventional computing to fulfil the demands of multi-functional electronics, growing across various technological domains. Memristors, two terminal devices that work based on resistance changes having memory capabilities, have gained popularity for neuromorphic computing due to their ability to emulate synaptic plasticity, a key aspect of learning and memory in biological brains. |
| 650 | __ | |aElectronic Science|2UGC |
| 650 | __ | |aPhysical and Basic Sciences|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Information Systems |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aSahoo, Narayan|eGuide |
| 700 | __ | |aPanda, Debashis|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/661206|yShodhganga |
| 905 | __ | |afromsg |
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