Emerging Artificial Neuron Devices for Probabilistic Computing

Li, Zongxiao; Geng, Xiaoying; Wang, Jian; Zhuge, Fei

doi:10.3389/fnins.2021.717947

Cited by 18 publications

(10 citation statements)

References 113 publications

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“…The memristor neuron is the most extensively developed device for artificial neurons and has been researched for the longest time. [50][51][52] Unlike memristor synapses that memorize the synaptic weight with nonvolatile switching characteristics, memristor neurons are mostly demonstrated with volatile switching devices, as they should not retain a certain resistance state for long period of time.…”

Section: Memristor Neuronmentioning

confidence: 99%

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Han

Yun

Lee

et al. 2022

Adv Funct Materials

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A spiking neural network (SNN) inspired by the structure and principles of the human brain can significantly enhance the energy efficiency of artificial intelligence computing by overcoming the bottlenecks of the conventional von Neumann architecture with its massive parallelism and spike transmissions. The construction of artificial neurons is important for the hardware implementation of an SNN, which generates spike signals when enough synaptic signals are gathered. Because circuit‐level artificial neurons with comparator and reset circuits require considerable hardware area, intensive efforts are devoted in recent years for building artificial neurons at the device level for better area efficiency. Furthermore, artificial sensory neuron devices, which perform neural processing and sensing concurrently, have recently been developed in order to reduce the hardware cost and energy consumption of traditional sensory systems through in‐sensor computing. This review article surveys and benchmarks the recent progress of artificial neuron devices for neural processing and sensing. First, various artificial neuron devices are summarized, including single‐transistor neurons (1T‐neurons), memristor neurons, phase‐change neurons, magnetic neurons, and ferroelectric neurons. Next, cointegration technologies with artificial synaptic devices and artificial sensory neurons for in‐sensor computing are introduced. Finally, the challenges and prospects for developing artificial neuron devices are discussed.

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Section: Memristor Neuronmentioning

confidence: 99%

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Han

Yun

Lee

et al. 2022

Adv Funct Materials

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“…Thus, ferroelectric transistors, which offers high energy efficiency and spike frequency due to a short refractory period, exhibit considerable potential for artificial neuron applications. [ 159 ] However, ferroelectric‐transistor‐based artificial neurons involve three‐terminal devices, making their implementation challenging in highly scaled neuromorphic arrays. Additionally, channel percolation in ferroelectric transistors, which is derived from the random distribution of switched domains, reduces the number of accessible states.…”

Section: Ferroelectric Transistors For Neuromorphic Applicationsmentioning

confidence: 99%

Ferroelectric Transistors for Memory and Neuromorphic Device Applications

Kim

Lee

2023

Advanced Materials

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Because a non-centrosymmetric crystal structure is mandatory for ferroelectric properties, very few materials are classified as ferroelectric. Historically, materials with complex crystal structures, such as BaTiO 3 (BTO), Pb[Zr x Ti 1−x ]O 3 (PZT), and Sr 2 Bi 2 TaO 9 (SBT), have been found to exhibit ferroelectricity. [5][6][7][8][9] Due to their spontaneous polarization, ferroelectric materials are diversely applied in memory devices, actuators, sensors, and energy storage. [1,2,[10][11][12][13] As the polarization state of ferroelectric materials can be regulated by an external electric field and the polarization is retained after the removal of the external electric field, such materials have the potential for applications involving high-performance non volatile memories. [9,14] However, some issues of conventional ferroelectric materials, such as high annealing temperatures, the requirement of noble electrode materials, and the diffusion of elements such as Pb complicate the integration of the previously reported ferroelectric materials into complementary metal-oxide semiconductors (CMOSs). [15][16][17][18] Furthermore, the large dielectric constants of perovskite-based ferroelectrics lead to high depolarization fields; [9,16] thus, the materials have short retention time and exhibit unstable polarization. [9] Additionally, the limited thickness scalability, complex etching process, and fatigue behaviors of perovskite-based ferroelectrics hinder the development of highly scaled ferroelectric devices. [9,18] Thus, semiconductor devices based on ferroelectric materials have limited commercial applications. Since the development of hafnia-based ferroelectrics, ferroelectric hafnia has attracted immense attention toward the development of semiconductor devices. [19][20][21] Compared to perovskite-based ferroelectric materials, hafnia-based ferroelectrics exhibit high scalability, and have high coercive electric fields (E c ) and low dielectric permittivity values, making them suitable for the fabrication of high-performance memory devices. [2,16,20,22] Additionally, hafnia is compatible with Si-based CMOS processes. Due to these advantages, hafnia-based ferroelectrics have been used in next-generation memory devices (such as ferroelectric capacitors, transistors, and tunnel junctions [FTJs]). [22][23][24][25][26] This paper reviews the recent findings and applications of hafnia-based ferroelectrics, highlighting the recent advances in ferroelectric transistors for next-generation memory and neuromorphic Ferroelectric materials have been intensively investigated for highperformance nonvolatile memory devices in the past decades, owing to their nonvolatile polarization characteristics. Ferroelectric memory devices are expected to exhibit lower power consumption and higher speed than conventional memory devices. However, non-complementary metal-oxidesemiconductor (CMOS) compatibility and degradation due to fatigue of traditional perovskite-based ferroelectric materials have hindered the development of high-density...

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“…The electronic implementation of the biological sensory memory system is required for intelligent electronics, and hence, extensive research has been conducted to mimic brain functionality. Despite remarkable progress in nanotechnology, a lot of work is required for the hardware-level implementation of artificial neural networks (ANNs) . To date, numerous electronic devices with resistive switching memory, , phase change memory, , and ferroelectric memory − have been developed to mimic the synaptic functionality of the human brain.…”

Section: Introductionmentioning

confidence: 99%

“…Despite remarkable progress in nanotechnology, a lot of work is required for the hardware-level implementation of artificial neural networks (ANNs). 8 To date, numerous electronic devices with resistive switching memory, 9,10 phase change memory, 11,12 and ferroelectric memory 13−15 have been developed to mimic the synaptic functionality of the human brain. Two-terminal devices, such as memristor-based artificial synapses, have a simple structure, low power consumption, and large device density.…”

Section: ■ Introductionmentioning

confidence: 99%

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Gupta

Kim

Park

et al. 2023

ACS Appl. Mater. Interfaces

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Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10 3 ) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

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Emerging Artificial Neuron Devices for Probabilistic Computing

Cited by 18 publications

References 113 publications

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Ferroelectric Transistors for Memory and Neuromorphic Device Applications

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

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