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A flexible artificial synapse for neuromorphic system
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, School of Electrical Engineering and Computer Science (EECS).ORCID iD: 0000-0002-1768-1071
Fudan University, SIST, State Key Laboratory of ASIC and System.
Fudan University, SIST, State Key Laboratory of ASIC and System.
KTH, School of Electrical Engineering and Computer Science (EECS). Fudan University, SIST, State Key Laboratory of ASIC and System.
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2018 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

Neuromorphic computing, as a new paradigm, highlighted for its highly parallel, energy efficient features, has attracted a lot of attention. The hardware implementation for a neuromorphic system proposes the strong desire for suitable building blocks. The synaptic device is a very promising solution because of its stimulation-history-related response, which fits the nature of a neural network. In this work, an artificial synapse based on a memristive transistor fabricated by a simple process is realized. The device not only shows multi-level states which is the main feature of a memristor and is essential to hardware implementation neuromorphic system, but also exhibits physical flexibility, a feature that supports wearable and portable electronics. On this basis, a proof-of-feasibility simulation using the experimental data is performed to realize the pattern classification.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2018.
Keywords [en]
Flexible, Memristive, Neuromorphic, Synapse
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-235722OAI: oai:DiVA.org:kth-235722DiVA, id: diva2:1252999
Conference
Conference on Electron Devices and Solid-State Circuits (EDSSC), Shenzhen, China, June 6-8, 2018.
Note

QC 20181008

Available from: 2018-10-03 Created: 2018-10-03 Last updated: 2018-10-10Bibliographically 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, KunlongSeoane, Fernando

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