First Demonstration of a Logic-Process Compatible Junctionless Ferroelectric FinFET Synapse for Neuromorphic Applications

Seo, Myungsoo; Kang, Min-Ho; Jeon, Seungwon; Bae, Hagyoul; Hur, Jae; Jang, Byung Chul; Yun, Seokjung; Cho, Seongwoo; Kim, Wu‐Kang; Kim, Myung‐Su; Hwang, Kyu‐Man; Hong, Seungbum; Choi, Sung‐Yool; Choi, Yang‐Kyu

doi:10.1109/led.2018.2852698

Cited by 137 publications

(78 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, there are opportunities to develop highly dense artificial synapse array by utilizing the junctionless feature of FinFETs. Recently, Choi and co‐workers have reported the first junctionless ferroelectric FinFET synapse with the HfZrO x (HZO) as the dielectric layer . The schematic diagram of this novel synaptic device, which combines advantages of the Junctionless FET and the Ferroelectric FET, is presented in Figure a.…”

Section: Ferroelectric‐gate Synaptic Transistorsmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Transistor‐Based Artificial Synapses

Dai

Zhao

Wang

et al. 2019

Adv Funct Materials

470

422

View full text Add to dashboard Cite

Simulating biological synapses with electronic devices is a re-emerging field of research. It is widely recognized as the first step in hardware building brain-like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor-based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor-based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor-based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor-based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor-based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor-based artificial synapses are also presented.In the nerve system, a synapse is a specialized structure that allows a neuron to pass chemical or electrical signals to another Figure 2. a) Schematic illustration (top) and microscopy image (bottom) of flexible synaptic transistors based on a random matrix of semiconducting CNTs. b) Case 1: the amplitudes of V LTP and V LTP are greater than other cases; thus, NL is the highest and ΔG is the largest. c) Case 2: the amplitudes of V LTP and V LTP are smaller than in case 1; thus, NL and ΔG are lower. d) Case 3: if the CNT transistor without the Au floating gate is used for the synaptic transistor, NL and ΔG are considerably smaller than in the other cases due to the limited charge storage space. Reproduced with permission. [87]

show abstract

Section: Ferroelectric‐gate Synaptic Transistorsmentioning

confidence: 99%

Section: Ferroelectric‐gate Synaptic Transistorsmentioning

confidence: 99%

Recent Advances in Transistor‐Based Artificial Synapses

Dai

Zhao

Wang

et al. 2019

Adv Funct Materials

470

422

View full text Add to dashboard Cite

show abstract

“…5a, is applied to the drain electrode to read out the level of weighted analog conductance. As a result, the writing and reading operations can be concurrently performed by the 3-terminal structure, which is Figure 5b (see Supplementary Information 9 for details pertaining to the neural network system) 49 . Recognition accuracy after each training epoch is shown in Figure 5c.…”

Section: Schematic Of the Array And Neural Network Simulationmentioning

confidence: 99%

All-solid-state Ion Synaptic Transistor for Wafer-scale Integration with Electrolyte of a Nanoscale Thickness

Lee

Kim

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Neuromorphic hardware computing is a promising alternative to von Neumann computing by virtue of its parallel computation, and low power consumption. To implement neuromorphic hardware based on deep neural network (DNN), a number of synaptic devices should be interconnected with neuron devices. For ideal hardware DNN, not only scalability and low power consumption, but also a linear and symmetric conductance change with the large number of conductance levels are required. Here an all-solid-state polymer electrolyte-gated synaptic transistor (pEGST) was fabricated on an entire silicon wafer with CMOS microfabrication and initiated chemical vapor deposition (iCVD) process. The pEGST showed good linearity as well as symmetry in potentiation and depression, conductance levels up to 8,192, and low switching energy smaller than 20 fJ/pulse. Selected 128 levels from 8,192 used to identify handwritten digits in the MNIST database with the aid of a multilayer perceptron, resulting in a recognition rate of 91.7 %.

show abstract

“…Recently, a bio-inspired neuromorphic system based on a spiking neural network (SNN) has been widely investigated because of its power-efficiency and parallel signal processing properties [2][3][4][5]. With regard to its application, the neuromorphic system, which is a hardware implementation of an artificial neural network, has been utilized mostly for pattern recognition [6][7][8][9][10], but also as a denoising auto encoder [11], for color image reconstruction [12], and for speech recognition [13]. In addition, various kinds of electronic devices have been studied as an artificial synaptic device, a crucial building block for constructing neuromorphic systems, including resistive switching materials [14][15][16][17], phase change materials [18][19][20], ferroelectric materials [21,22], and transistors [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Solving Overlapping Pattern Issues in On-Chip Learning of Bio-Inspired Neuromorphic System with Synaptic Transistors

Kim

Park

2019

Electronics

View full text Add to dashboard Cite

Recently, bio-inspired neuromorphic systems have been attracting widespread interest thanks to their energy-efficiency compared to conventional von Neumann architecture computing systems. Previously, we reported a silicon synaptic transistor with an asymmetric dual-gate structure for the direct connection between synaptic devices and neuron circuits. In this study, we study a hardware-based spiking neural network for pattern recognition using a binary modified National Institute of Standards and Technology (MNIST) dataset with a device model. A total of three systems were compared with regard to learning methods, and it was confirmed that the feature extraction of each pattern is the most crucial factor to avoiding overlapping pattern issues and obtaining a high pattern classification ability.

show abstract

First Demonstration of a Logic-Process Compatible Junctionless Ferroelectric FinFET Synapse for Neuromorphic Applications

Cited by 137 publications

References 17 publications

Recent Advances in Transistor‐Based Artificial Synapses

Recent Advances in Transistor‐Based Artificial Synapses

All-solid-state Ion Synaptic Transistor for Wafer-scale Integration with Electrolyte of a Nanoscale Thickness

Solving Overlapping Pattern Issues in On-Chip Learning of Bio-Inspired Neuromorphic System with Synaptic Transistors

Contact Info

Product

Resources

About