Organic nano-floating-gate transistor memory with metal nanoparticles

Tho, Luu Van; Baeg, Kang‐Jun; Noh, Yong‐Young

doi:10.1186/s40580-016-0069-7

Cited by 50 publications

(34 citation statements)

References 47 publications

(36 reference statements)

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“…Floating‐gate transistors usually have similar device structures as compared to conventional field‐effect transistors (FETs), except for the addition of the control gate that is usually embedded in the dielectric layer . The gate bias accumulated charges (electrons or holes) in the channel of traditional FETs will be dissipated quickly and completely with the remove of gate voltage.…”

Section: Floating‐gate Synaptic Transistorsmentioning

confidence: 99%

“…Therefore, accumulated charges in the channel of traditional FETs usually exhibit a “volatile” behavior. As for floating‐gate transistors, during the gate programming process, electronic charges can be easily injected into the floating gate based on thermal emission or quantum tunneling . Due to the existence of the robust charge blocking and tunneling layer, the trapped charges can be stored nonvolatilely.…”

Section: Floating‐gate Synaptic Transistorsmentioning

confidence: 99%

“…The use of continuous and conducting film based floating‐gate in synaptic devices is usually limited by their poor charge retention ability owing to the lateral leakage, and the continuous floating gate synaptic devices may also suffer from the increased cell‐to‐cell interferences when the device is scaling down . The use of metallic or semiconducting nanoparticles as the floating gate layer may have advantages of discontinued and separated charge elements.…”

Section: Floating‐gate Synaptic Transistorsmentioning

confidence: 99%

See 2 more Smart Citations

Recent Advances in Transistor‐Based Artificial Synapses

Dai

Zhao

Wang

et al. 2019

Adv Funct Materials

444

422

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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]

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Section: Floating‐gate Synaptic Transistorsmentioning

confidence: 99%

Section: Floating‐gate Synaptic Transistorsmentioning

confidence: 99%

Section: Floating‐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

444

422

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“…Electrical pulses can induce the movement of charged particles (proton, electrons and holes) in the materials. These carriers can then be captured by various trap centers [75]. Mimicking of synaptic functions can be realized based on [78] the process of carriers capture and release [76,77].…”

Section: Capture and Release Of Carriersmentioning

confidence: 99%

Memristive Artificial Synapses for Neuromorphic Computing

et al. 2021

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Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

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“…The floating gate OTFMTs structure is the most popular of them all because of the well-established operation mechanism, high memory capacity, reliable memory operations, charge storage stability for long time, and simple single transistor structure [7,8]. A non-volatile memory device has a structure of metal-oxide-semiconductor field effect transistor, the gate electrode is modified to store charges.…”

Section: Introductionmentioning

confidence: 99%

Improved Memory Properties of Graphene Oxide-Based Organic Memory Transistors

et al. 2019

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To investigate the behaviour of the organic memory transistors, graphene oxide (GO) was utilized as the floating gate in 6,13-Bis(triisopropylsilylethynyl)pentacene (TIPS-pentacene)-based organic memory transistors. A cross-linked, off-centre spin-coated and ozone-treated poly(methyl methacrylate) (cPMMA) was used as the insulating layer. High mobility and negligible hysteresis with very clear transistor behaviour were observed for the control transistors. On the other hand, memory transistors exhibited clear large hysteresis which is increased with increasing programming voltage. The shifts in the threshold voltage of the transfer characteristics as well as the hysteresis in the output characteristics were attributed to the charging and discharging of the floating gate. The counter-clockwise direction of hysteresis indicates that the process of charging and discharging the floating gate take place through the semiconductor/insulator interface. A clear shift in the threshold voltage was observed when different voltage pulses were applied to the gate. The non-volatile behaviour of the memory transistors was investigated in terms of charge retention. The memory transistors exhibited a large memory window (~30 V), and high charge density of (9.15 × 1011 cm−2).

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Organic nano-floating-gate transistor memory with metal nanoparticles

Cited by 50 publications

References 47 publications

Recent Advances in Transistor‐Based Artificial Synapses

Recent Advances in Transistor‐Based Artificial Synapses

Memristive Artificial Synapses for Neuromorphic Computing

Improved Memory Properties of Graphene Oxide-Based Organic Memory Transistors

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