Orientation selectivity with organic photodetectors and an organic electrochemical transistor

Gkoupidenis, Paschalis; Rezaei‐Mazinani, Shahab; Proctor, Christopher M.; Ismailova, Esma; Malliaras, George G.

doi:10.1063/1.4967947

Cited by 29 publications

(23 citation statements)

References 28 publications

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“…Recently, organic semiconductors have also been used to fabricate electrolyte‐gate synaptic transistors . Malliaras and co‐workers have done a very systematic study in the implementation of biological synaptic functions with PEDOT:PSS . Figure a presents the schematic diagram of the PEDOT:PSS‐based synaptic transistors that are laterally gated through a KCl electrolyte .…”

Section: Electrolyte‐gate Synaptic Transistorsmentioning

confidence: 99%

Recent Advances in Transistor‐Based Artificial Synapses

Dai

Zhao

Wang

et al. 2019

Adv Funct Materials

470

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: Electrolyte‐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

“…99,100 Right: The response of organic electrochemical transistors (OECTs) is spatiotemporal-the response time of OECTs depends on the distance between the input (gate) and output (drain) terminals. 117,118 (b) Ionic/molecular recognition. Left: In a neuronal membrane, voltage-and neurotransmitter-gated ionic channels offer ionic and molecular recognition.…”

Section: Biophysical Realismmentioning

confidence: 99%

“…The response of such a device depends not only on time, but also on the distance between the device terminals. 117,118 As a result of this, spatially distributed voltage inputs on the gate electrode array correlate with the output of the transistor leading to the ability of the multi-terminal device to discriminate between different stimuli orientations. This behavior is analogous to biological phenomena, such as the ability of the primary visual cortex to detect edges from visual stimuli.…”

Section: Biophysical Realismmentioning

confidence: 99%

Organic materials and devices for brain-inspired computing: From artificial implementation to biophysical realism

Burgt

Gkoupidenis

2020

MRS Bull.

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“…These neuromorphic systems emulating simple biological senses have motivated both life and industry in various aspects. Light cognition is one of the most significant sensory functions for neuromorphic systems, which is essential for further development in image recognition, detection, optical wireless communication, and operation . The development of photonic synapses for neuromorphic systems is expected to facilitate the evolution of mimicking neural networks and functions of the retina, which allows to perform the real‐time image processing on the front end to improve the efficiency of image processing and the quality.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Photonic Synapses for Neuromorphic Systems

Zhang

Dai

Zhao

et al. 2020

Advanced Intelligent Systems

165

118

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Implementing synaptic functions with electronic devices is critical to achieve neuromorphic systems on the hardware platform, as synapses play important roles in brain computing and memory. Synapses modulated by light signals, which are also referred as photonic synapses, can not only make effective use of the outstanding properties of light to provide devices with ultrahigh propagation speed, high bandwidth and low crosstalk but also provide a noncontact writing method, which can facilitate the evolution of optical wireless communication and operation. More importantly, real‐time image processing can also be performed by photonic synapses which possess temporary memory. Thus far, tremendous efforts have been taken to design and fabricate photonic synapses. Herein, a summary of the development of different kinds of emerging materials utilized in photonic synaptic devices including memristors, field‐effect transistors, and phase change memory is presented, followed by the innovative applications of photonic synapses for neuromorphic systems. Finally, some current challenges and future study directions are discussed.

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Orientation selectivity with organic photodetectors and an organic electrochemical transistor

Cited by 29 publications

References 28 publications

Recent Advances in Transistor‐Based Artificial Synapses

Recent Advances in Transistor‐Based Artificial Synapses

Organic materials and devices for brain-inspired computing: From artificial implementation to biophysical realism

Recent Progress in Photonic Synapses for Neuromorphic Systems

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