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Universal and Convenient Optimization Strategies for Three-Terminal Memristors
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).ORCID iD: 0000-0002-1768-1071
tate Key Laboratory of ASIC and System, SIST, Fudan University, Shanghai, 200433, China.
Fudan University, SIST, State Key Laboratory of ASIC and System.
Fudan University, SIST, State Key Laboratory of ASIC and System.
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2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 48815-48826, article id 8454450Article in journal (Refereed) Published
Abstract [en]

Neuromorphic computing, i.e., brainlike computing, has attracted a great deal of attention because of its exceptional performance. For the hardware implementation of neuromorphic systems, the desired key building blocks, artificial synapses, have been intensively investigated recently. However, many issues, such as the small state number, low reliability, and high energy consumption, have complicated the path to real applications. Therefore, methods that can improve the performance of the artificial synapses are highly desired. Although different artificial synapses have diverse working mechanisms, universal opti- mization strategies that can be applied to most three-terminal field-effect-transistor-type artificial synapses are proposed in this paper. Instead ofwasting the third terminal in the device structure, the working condition can be effectively tuned by this third terminal. The key parameters, such as the gate electric field intensity and distribution, can be adjusted, and the performance is thereby tuned. In this manner, multiple performance metrics are optimized, such as the current change per pulse (ΔI), the linearity, the uniformity, and the power consumption. The mechanisms behind these strategies are also investigated to strengthen the effectiveness. This paper will push the performance of the current artificial synapses to a new level.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 6, p. 48815-48826, article id 8454450
Keywords [en]
Memristors, optimization methods, neuromorphics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-235715DOI: 10.1109/ACCESS.2018.2866930ISI: 000445491300001Scopus ID: 2-s2.0-85052888437OAI: oai:DiVA.org:kth-235715DiVA, id: diva2:1252991
Note

QC 20181129

Available from: 2018-10-03 Created: 2018-10-03 Last updated: 2018-11-29Bibliographically approved
In thesis
1. Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems
Open this publication in new window or tab >>Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the past decade, the field of personal electronic systems has trended toward mobile and wearable devices. However, the capabilities of existing electronic systems are overwhelmed by the computing demands at the wearable sensing stage. Two main bottlenecks are encountered. The first bottleneck is located within the computing module, between the processing units and the memory, and is known as the von-Neumann bottleneck. The second bottleneck is located between the sensing module and the computing module of the system.

Inspired by neuromorphic computing, an architecture of the sensitive neuromorphic network (SNN) is developed as a candidate for overcoming both bottlenecks. Suitable building blocks, especially in flexible form, must be developed. In this work, starting from the demand analysis and followed by prototype development, performance optimization, and feasibility testing, two kinds of critical devices were developed for fabricating a photosensitive neuromorphic network (PSNN).

A high-performance flexible electrical artificial synapse that is based on the electron-trapping mechanism was developed. In addition to the basic memristive features, multiple kinds of synaptic plasticity were also demonstrated, which enriched the collection of possible applications. Furthermore, optimization on multiple performance metrics was easily performed using the intrinsic features and structure of the device.

A new photoelectrical artificial synapse was also realized by successfully combining light signal sensing and processing in a single synapse. A flexible dual-mode photoelectrical synapse, which fulfilled the requirements of the designed PSNN working protocol, was demonstrated. The device showed gate-tunable photomemristive features, thereby enabling its application as a photoelectrical artificial synapse.

Using the newly developed devices and the proposed network architecture, this work successfully initiated a new area of research, namely, the sensitive neuromorphic network, and provided a valid solution that addresses the current limitations of existing wearable electronic systems.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 58
Series
TRITA-CBH-FOU ; 2018:46
Keywords
Flexible electronics, Neuromorphic network, Memristor, Electron trapping, Electrical artificial synapse, Photoelectrical artificial synapse
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Applied Medical Technology
Identifiers
urn:nbn:se:kth:diva-235742 (URN)978-91-7729-979-0 (ISBN)
Public defence
2018-10-31, T2, Hälsovägen 11C, Huddinge, 09:00 (English)
Opponent
Supervisors
Note

QC 20181008

Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2018-10-08Bibliographically approved

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Yang, KunlongHuan, YuxiangZheng, Li-rongSeoane, Fernando

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