Two- and three-terminal HfO2-based multilevel resistive memories for neuromorphic analog synaptic elements. Issue 2 (20th October 2021)
- Record Type:
- Journal Article
- Title:
- Two- and three-terminal HfO2-based multilevel resistive memories for neuromorphic analog synaptic elements. Issue 2 (20th October 2021)
- Main Title:
- Two- and three-terminal HfO2-based multilevel resistive memories for neuromorphic analog synaptic elements
- Authors:
- Kang, Heebum
Park, Jinah
Lee, Dokyung
Kim, Hyun Wook
Jin, Sol
Ahn, Minjoon
Woo, Jiyong - Abstract:
- Abstract: Synaptic elements based on memory devices play an important role in boosting neuromorphic system performance. Here, we show two types of fab-friendly HfO2 material-based resistive memories categorized by configuration and an operating principle for a suitable analog synaptic device aimed at inference and training of neural networks. Since the inference task is mainly related to the number of states from a recognition accuracy perspective, we first demonstrate multilevel cell (MLC) properties of compact two-terminal resistive random-access memory (RRAM). The resistance state can be finely subdivided into an MLC by precisely controlling the evolution of conductive filament constructed by the local movement of oxygen vacancies. Specifically, we investigate how the thickness of the HfO2 -switching layer is related to an MLC, which is understood by performing physics-based modeling in MATLAB from a microscopic view. Meanwhile, synaptic devices driven by an interfacial switching mechanism instead of local filamentary dynamics are preferred for training accelerated neuromorphic systems, where the analogous transition of each state ensures high accuracy. Thus, we introduce three-terminal electrochemical random-access memory that facilitates mobile ions across the entire HfO2 switching area uniformly, resulting in highly controllable and gradually tuned current proportional to the amount of migrated ions.
- Is Part Of:
- Neuromorphic computing and engineering. Volume 1:Issue 2(2021)
- Journal:
- Neuromorphic computing and engineering
- Issue:
- Volume 1:Issue 2(2021)
- Issue Display:
- Volume 1, Issue 2 (2021)
- Year:
- 2021
- Volume:
- 1
- Issue:
- 2
- Issue Sort Value:
- 2021-0001-0002-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-10-20
- Subjects:
- neuromorphic computing -- synaptic device -- resistive switching memory -- electrochemical random-access memory
Neural networks (Computer science) -- Periodicals
Neural computers -- Periodicals
Neuromorphics -- Periodicals
006.3 - Journal URLs:
- http://www.iop.org/ ↗
https://iopscience.iop.org/journal/2634-4386 ↗ - DOI:
- 10.1088/2634-4386/ac29ca ↗
- Languages:
- English
- ISSNs:
- 2634-4386
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20957.xml