Conductive-bridging random-access memories for emerging neuromorphic computing. Issue 27 (6th May 2020)
- Record Type:
- Journal Article
- Title:
- Conductive-bridging random-access memories for emerging neuromorphic computing. Issue 27 (6th May 2020)
- Main Title:
- Conductive-bridging random-access memories for emerging neuromorphic computing
- Authors:
- Cha, Jun-Hwe
Yang, Sang Yoon
Oh, Jungyeop
Choi, Shinhyun
Park, Sangsu
Jang, Byung Chul
Ahn, Wonbae
Choi, Sung-Yool - Abstract:
- Abstract : This review covers CBRAM-based artificial synapses and neurons towards emerging computing applications from the operation principles of CBRAMs to state-of-the-art experimental demonstrations. Abstract : With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal–oxide–semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on–off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave theAbstract : This review covers CBRAM-based artificial synapses and neurons towards emerging computing applications from the operation principles of CBRAMs to state-of-the-art experimental demonstrations. Abstract : With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal–oxide–semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on–off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems. … (more)
- Is Part Of:
- Nanoscale. Volume 12:Issue 27(2020)
- Journal:
- Nanoscale
- Issue:
- Volume 12:Issue 27(2020)
- Issue Display:
- Volume 12, Issue 27 (2020)
- Year:
- 2020
- Volume:
- 12
- Issue:
- 27
- Issue Sort Value:
- 2020-0012-0027-0000
- Page Start:
- 14339
- Page End:
- 14368
- Publication Date:
- 2020-05-06
- Subjects:
- Nanoscience -- Periodicals
Nanotechnology -- Periodicals
620.505 - Journal URLs:
- http://www.rsc.org/Publishing/Journals/NR/Index.asp ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d0nr01671c ↗
- Languages:
- English
- ISSNs:
- 2040-3364
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9830.266000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 13824.xml