Effect of Initial Synaptic State on Pattern Classification Accuracy of 3D Vertical Resistive Random Access Memory (VRRAM) Synapses
- 주제(키워드) Vertical Resistive RAM , Neuromorphics , Neural Network Hardware , Guide Training Algorithm
- 주제(기타) Chemistry, Multidisciplinary
- 주제(기타) Nanoscience & Nanotechnology
- 주제(기타) Materials Science, Multidisciplinary
- 주제(기타) Physics, Applied
- 주제(기타) Physics, Condensed Matter
- 설명문(일반) [Sun, Wookyung; Choi, Sujin; Kim, Bokyung; Shin, Hyungsoon] Ewha Womans Univ, Dept Elect & Elect Engn, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 AMER SCIENTIFIC PUBLISHERS
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000169425
- 본문언어 영어
- Published As https://dx.doi.org/10.1166/jnn.2020.17798
- PubMed https://pubmed.ncbi.nlm.nih.gov/32126648
초록/요약
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.
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