Phase‐Controlled Artificial SiZnSnO/P(VDF‐TrFE) Synaptic Devices with a High Dynamic Range for Neuromorphic Computing

Lee, Byeong Hyeon; Lee, Ji Ye; Kumar, Akash; Lee, Sang Yeol

doi:10.1002/aelm.202200810

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…As shown in Figure f, the maximum recognition rate for our FeFET synaptic device was 85.4%, which is comparable to previous studies on other synaptic devices (see Supporting Information Table S2). − …”

Section: Resultsmentioning

confidence: 99%

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Kim,

Oh,

Choo

et al. 2023

ACS Nano

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Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS 2 )/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.

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Section: Resultsmentioning

confidence: 99%

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Kim,

Oh,

Choo

et al. 2023

ACS Nano

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“…Overall, increasing the thickness of the LATP layer improved the linearity characteristics of the conductance modulation, resulting in a higher pattern recognition accuracy and stability in neuromorphic systems with on-chip learning. Finally, to compare the synaptic characteristics of our synaptic transistors with those of other inorganic semiconductor-based synaptic transistor devices, the nonlinearity values and recognition accuracy obtained in this study and recently reported studies are summarized in Table . − Compared with other studies, the proposed device exhibits better linearity characteristics and a high pattern recognition accuracy (94.53%) owing to the optimization of the LATP layer with a high ionic conductivity.…”

Section: Resultsmentioning

confidence: 85%

All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems

Park,

Hwang,

Song

et al. 2023

ACS Appl. Mater. Interfaces

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Neuromorphic computing, an innovative technology inspired by the human brain, has attracted increasing attention as a promising technology for the development of artificial intelligence systems. This study proposes synaptic transistors with a Li1–x Al x Ti2–x (PO4)3 (LATP) layer to analyze the conductance modulation linearity, which is essential for weight mapping and updating during on-chip learning processes. The high ionic conductivity of the LATP electrolyte provides a large hysteresis window and enables linear weight update in synaptic devices. The results demonstrate that optimizing the LATP layer thickness improves the conductance modulation and linearity of synaptic transistors during potentiation and degradation. A 20 nm-thick LATP layer results in the most nonlinear depression (αd = −6.59), whereas a 100 nm-thick LATP layer results in the smallest nonlinearity (αd = −2.22). Additionally, a device with the optimal 100 nm-thick LATP layer exhibits the highest average recognition accuracy of 94.8% and the smallest fluctuation, indicating that the linearity characteristics of a device play a crucial role in weight update during learning and can significantly affect the recognition accuracy.

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“…The first approach controlled the carrier density of a semiconducting channel using the polarization field from a ferroelectric material. [33,34,[36][37][38][39][119][120][121][122][123][124] We note that ferroelectric materials have certain domains that have different polarization directions along their easy axis. The continuous change in the polarization field from the ferroelectric domains enables multiple conductivity states in the semiconducting channel, which can be used as a synaptic device and learning process.…”

Section: Ferroelectric Gating Of 2d Semiconducting Channelsmentioning

confidence: 87%

Memory and Synaptic Devices Based on Emerging 2D Ferroelectricity

Joo

Hwang

Hong

et al. 2023

Adv Elect Materials

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Memory devices are an essential part of modern electronics. Efforts to move beyond the traditional "read" and "write" of digital information in volatile and non-volatile memory devices are leading to the rapid growth of neuromorphic technology. There is a growing demand for memory devices with continuous memory states with various retention times and greater integration density with more energy-efficient mechanisms. Two types of memory devices (i.e., non-volatile digital memory and neuro-synaptic devices) have been extensively investigated with emerging materials. Among numerous materials for such memory devices, in this review, the authors focus on 2D ferroelectric materials for promising memory and synaptic devices. Three types of memory devices based on 2D ferroelectric materials are classified and discussed here: 1) ferroelectric gating of semiconducting channels, 2) active ferroelectric channels, and 3) ferroelectric tunnel junction devices. It is known that atomically thin geometry competes with ferroelectricity, which can degrade the quality of the devices based on atomically thin ferroelectric materials. Various efforts to resolve the fundamental issue with emerging 2D ferroelectric materials and how they can be used as a critical element for memory and synaptic devices are surveyed.

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Phase‐Controlled Artificial SiZnSnO/P(VDF‐TrFE) Synaptic Devices with a High Dynamic Range for Neuromorphic Computing

Cited by 4 publications

References 43 publications

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems

Memory and Synaptic Devices Based on Emerging 2D Ferroelectricity

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