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Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Artificial synapses (ASs) are electronic devices emulating important functions of biological synapses, which are essential building blocks of artificial neuromorphic networks for brain-inspired computing. A human brain consists of several quadrillion synapses for information storage and processing, and massively parallel computation. Neuromorphic systems require ASs to mimic biological synaptic functions, such as paired-pulse facilitation, short-term potentiation, long-term potentiation, spatiotemporally-correlated signal processing, and spike-timing-dependent plasticity, etc. Feature size and energy consumption of ASs need to be minimized for high-density energy-efficient integration. This work reviews recent progress on ASs. First, synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed. Recent advances in flexible artificial synapses for artificial sensory nerves are also briefly introduced. Finally, challenges and opportunities in the field are discussed., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows changes in synaptic strength that make a brain capable of learning from experience. During development of neuromorphic electronics, great efforts have been made to design and fabricate electronic devices that emulate synapses. Three-terminal artificial synapses have the merits of concurrently transmitting signals and learning. Inorganic and organic electronic synapses have mimicked plasticity and learning. Optoelectronic synapses and photonic synapses have the prospective benefits of low electrical energy loss, high bandwidth, and mechanical robustness. These artificial synapses provide new opportunities for the development of neuromorphic systems that can use parallel processing to manipulate datasets in real time. Synaptic devices have also been used to build artificial sensory systems. Here, recent progress in the development and application of three-terminal artificial synapses and artificial sensory systems is reviewed., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Artificial synapses are a key component of neuromorphic computing systems. To achieve high‐performance neuromorphic computing ability, a huge number of artificial synapses should be integrated because the human brain has a huge number of synapses (≈1015). In this study, a coplanar synaptic, thin‐film transistor (TFT) made of c‐axis‐aligned crystalline indium gallium tin oxide (CAAC–IGTO) is developed. The electrical characteristics of the biological synapses such as inhibitory postsynaptic current (IPSC), paired‐pulse depression (PPD), short‐term plasticity (STP), and long‐term plasticity at VDS = 0.1 V, are demonstrated. The measured synaptic behavior can be explained by the migration of positively charged oxygen vacancies (Vo+/Vo++) in the CAAC–IGTO layer. The mechanism of implementing synaptic behavior is completely new, compared to previous reports using electrolytes or ferroelectric gate insulators. The advantage of this device is to use conventional gate insulators such as SiO2 for synaptic behavior. Previous studies use chitosan, Ta2O3, SiO2 nanoparticles , Gd2O3, and HfZrOx for gate insulators, which cannot be used for high integration of synaptic devices. The metal–oxide TFTs, widely used in the display industry, can be applied to the synaptic transistors. Therefore, CAAC–IGTO synaptic TFT can be a good candidate for application as an artificial synapse for highly integrated neuromorphic chips. [ABSTRACT FROM AUTHOR]

Metal oxide semiconductors thin film transistors (TFTs) have become important candidates for neuromorphic computing applications by simulating the functions of biological synapses. In this article, optoelectronic synaptic transistors with tunable plasticity are achieved by utilizing printed indium tin oxide (ITO) channel and Ag/ITO dual-layer contact electrodes. The Ag/ITO dual-layer electrodes not only provide ITO TFTs with high electrical performance but enabled them with tunable plasticity. Under the synergistic electrical and optical modulation, both the short- and long-term plasticity in the printed ITO synaptic TFTs are emulated. These achievements are of great significance for applying printed synaptic transistors in optoelectronic neuromorphic hardware. [ABSTRACT FROM AUTHOR]

Brain‐inspired associative memory is meaningful for pattern recognitions and image/speech processing. Here, a ferroelectric synaptic transistor network is proposed that is capable of associative learning and one‐step recalling of a whole set of data from only partial information. The competition between an external field and the internal depolarization field governs the ferroelectric creep of domain walls and offers each single ferroelectric synapse a full and subfemtojoule‐energy‐cost Hebbian synaptic plasticity, including short‐term memory (STM) to long‐term memory (LTM) transition, and remarkably both spike‐timing‐dependent plasticity (STDP) and spike‐rate‐dependent plasticity (SRDP). Assisted by the third terminal to control the ferroelectric domain dynamics, self‐adaptive coupling between neurons is realized by updating synaptic weight concurrently. Pavlov's dog experiment and multiassociative memories are demonstrated in this ferroelectric synaptic transistor network. Such ferroelectric synaptic transistor network is available for building multilayer neural networks and provides new avenues for associative‐memory information processing. [ABSTRACT FROM AUTHOR]

Development of artificial synapses is essential for highly-efficient brain-inspired neuromorphic computing. To achieve the high-performance artificial synapses, gradual change in synaptic weight with linear and symmetric forms is required. Here, we propose artificial synapses, in which synaptic weight is changed gradually by use of gate bias to modulate the Schottky barrier height between reduced graphene oxide and oxide semiconductor. This approach enables linear and symmetric change in synaptic weight. Also, an ion gel is used as the gate electrolyte, which can reduce the gate voltage required for modulation of Schottky barrier height. The fabricated artificial synapses show essential synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, and potentiation/depression. These results demonstrate that the devices that exploit modulation of Schottky barrier height can be applied to artificial synapses in advanced neuromorphic computing. Image 1 [ABSTRACT FROM AUTHOR]

Emulating the essential synaptic working principles using a three-terminal synaptic transistor has been an important research field in recent years. Electrospun metal oxide nanofibers have been regarded as promising blocks for large-area, low-cost, and one-dimensional (1D) electronic devices. In this letter, zinc-tin oxide (ZnSnO) nanofibers were fabricated by electrospinning, and the synaptic transistors based on ZnSnO nanofibers were integrated. To mimic the synaptic behavior of human brain, LiClO4 dissolved in polyethylene oxide was used as the gate electrolyte in the synaptic transistors. The plasticities, including short term and long term, are mimicked with the help of electrolyte ion dynamics under low and large bias voltage, and an energy consumption of as low as 0.44 pJ per event is observed. This work demonstrates a new approach for establishing the large-scale, energy-efficient 1D artificial synapses for neuromorphic networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor (CMOS)‐based processors can potentially solve a variety of problems being faced by today's artificial intelligence (AI) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of AI‐based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS‐based multiply–accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross‐bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future. [ABSTRACT FROM AUTHOR]