Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신형순 | * |
dc.contributor.author | 선우경 | * |
dc.date.accessioned | 2020-04-13T16:30:14Z | - |
dc.date.available | 2020-04-13T16:30:14Z | - |
dc.date.issued | 2020 | * |
dc.identifier.issn | 1533-4880 | * |
dc.identifier.issn | 1533-4899 | * |
dc.identifier.other | OAK-26724 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/253781 | - |
dc.description.abstract | Amidst the considerable attention artificial intelligence (Al) has attracted in recent years, a neuromorphic chip that mimics the biological neuron has emerged as a promising technology. Memristor or Resistive random-access memory (RRAM) is widely used to implement a synaptic device. Recently, 3D vertical RRAM (VRRAM) has become a promising candidate to reducing resistive memory bit cost. This study investigates the operation principle of synapse in 3D VRRAM architecture. In these devices, the classification response current through a vertical pillar is set by applying a training algorithm to the memristors. The accuracy of neural networks with 3D VRRAM synapses was verified by using the HSPICE simulator to classify the alphabet in 7 x 7 character images. This simulation demonstrated that 3D VRRAMs are usable as synapses in a neural network system and that a 3D VRRAM synapse should be designed to consider the initial value of the memristor to prepare the training conditions for high classification accuracy. These results mean that a synaptic circuit using 3D VRRAM will become a key technology for implementing neural computing hardware. | * |
dc.language | English | * |
dc.publisher | AMER SCIENTIFIC PUBLISHERS | * |
dc.subject | Vertical Resistive RAM | * |
dc.subject | Neuromorphics | * |
dc.subject | Neural Network Hardware | * |
dc.subject | Guide Training Algorithm | * |
dc.title | Effect of Initial Synaptic State on Pattern Classification Accuracy of 3D Vertical Resistive Random Access Memory (VRRAM) Synapses | * |
dc.type | Article | * |
dc.relation.issue | 8 | * |
dc.relation.volume | 20 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 4730 | * |
dc.relation.lastpage | 4734 | * |
dc.relation.journaltitle | JOURNAL OF NANOSCIENCE AND NANOTECHNOLOGY | * |
dc.identifier.doi | 10.1166/jnn.2020.17798 | * |
dc.identifier.wosid | WOS:000518698800022 | * |
dc.author.google | Sun, Wookyung | * |
dc.author.google | Choi, Sujin | * |
dc.author.google | Kim, Bokyung | * |
dc.author.google | Shin, Hyungsoon | * |
dc.contributor.scopusid | 신형순(7404012125) | * |
dc.contributor.scopusid | 선우경(7404011223) | * |
dc.date.modifydate | 20240322125227 | * |