Tunable Resistive Switching Enabled by Malleable Redox Reaction in the Nano-Vacuum Gap
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Neuromorphic computing has emerged as a highly promising alternative to conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neurons with leaky integrate-and-fire behavior and artificial synapses with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between a bottom electrode and a mixed-ionic–electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filaments dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filaments by tuning the electron tunneling efficiency, in analogy to a pseudo-three-terminal device, resulting in tunable switching behavior in various forms from volatile to nonvolatile switching in a single device. Our device demonstrated cross-functions, in particular, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building large-scale artificial neural networks for neuromorphic computing.Keywords:
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Resistive touchscreen
Neuromorphic (i.e., brain-like) computing aims to circumvent the limitations of von Neumann architectures by spatially co-locating processor and memory blocks or even combining logic and data storage functions within the same device. Neuromorphic devices also have the potential to provide efficient architectures for image recognition, machine learning, and artificial intelligence. With this motivation in mind, this paper will explore how the unique materials properties of two-dimensional (2D) materials enable opportunities for novel gate-tunable neuromorphic devices.
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One of the main tasks in neuromorphic systems would be the design of neuromorphic devices, capable of mimicking multiple fundamental synaptic computation behaviors. Moreover, mimicking dendrite integration behaviors on a single neuromorphic device would be highly suitable for neuromorphic engineering. Recently, the study on solid-state neuromorphic devices leads to the emergence of a new branch of neuromorphic engineering owing to its great potentials. With the developments of new materials technology and new conceptual devices, several kinds of neuromorphic devices have been proposed, including two terminal resistance switch devices and three terminal transistors. Moreover, memtransistors have been reported with interesting neuromorphic functions. Furthermore, multi-electrodes have been integrated within these devices, demonstrating advanced neural functions. Especially, multi-gates could be assembled in a single transistor, exhibiting interesting dendritic integration functions and dendritic algorithm. Moreover, important neural cognitive behaviors have been mimicked on these multiterminal devices. All these results indicate their great potentials in neuromorphic engineering.
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We summarize our progress towards monolithic and analog neuromorphic computing utilizing domain wall-magnetic tunnel junction (DW-MTJ) devices. We have previously shown device performance for binary logic DW-MTJ devices. Here, we expand on that work by demonstrating neuromorphic functionality using shape-dependent tunability. We measure multi-weight synapses and stochastic neurons monolithically fabricated from the same material stack, enabling future integrated neuromorphic circuits. Future work includes fabrication of leaky integrate and fire (LIF) neurons to complete the library of neuromorphic functionality for a full DW-MTJ crossbar array capable of neuromorphic computing.
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This article discusses neuromorphic medical electronics including biological computation, neuromorphic coding, neuromorphic hardware and neuromorphic instrumentation. Two examples are given from the auditory system: a silicon cochlea and a neuromorphic cochlea implant.
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Conventional von Neumann–based computing systems have inherent limitations such as high hardware complexity, relatively inferior energy efficiency, and low bandwidth. As an alternative, neuromorphic computation is emerging as a platform for next‐generation artificial intelligence computing systems due to their potential advantages such as highly energy‐efficient computing, robust learning, fault tolerance, and parallel processing. Moreover, to further enhance the energy efficiency and processing speed, photonic‐based neuromorphic systems have gathered significant interest in the past few years. Herein, the recent progress and development of optoelectronic and all‐optical neuromorphic devices is summarized, focusing on their structures, materials, and potential applications. Particularly, for optoelectronic neuromorphic devices, devices with planar and vertical structures are described along with their key strategies in materials and device structures. Next, all‐optical memory and neuromorphic devices for neuromorphic computing are reviewed. Finally, the applications of optoelectronic neuromorphic devices are discussed for their potential utilization in neuromorphic computing systems.
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Abstract Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low‐dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light‐matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two‐terminal neuromorphic memristors, three‐terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low‐dimensional materials is prospected.
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Neuromorphic computing or neuromorphic engineering is an engineering discipline that attempts to simulate human brain function by creating circuits that mimic the shape of neurons. In the field of neuromorphic computing, neuromorphic processors are used. There are many types of neuromorphic processors, and there are neuromorphic processors implemented based on FPGAs. Neuromorphic processors use an artificial intelligence model called a spiking neural network. Each neuromorphic processor has different characteristics. For example, the spiking neural network model supported by each supported neuromorphic processor may be different. In this paper, we propose a service architecture named NAAL (Neuromorphic Architecture Abstract Layer) that enables the use of spiking neural networks by virtualizing various neuromorphic processors with different characteristics.
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Abstract Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems. At the core of neuromorphic computing are neuromorphic devices that mimic the functions and dynamics of neurons and synapses, enabling the hardware implementation of artificial neural networks. Various types of neuromorphic devices have been proposed based on different physical mechanisms such as resistive switching devices and electric-double-layer transistors. These devices have demonstrated a range of neuromorphic functions such as multistate storage, spike-timing-dependent plasticity, dynamic filtering, etc. To achieve high performance neuromorphic computing systems, it is essential to fabricate neuromorphic devices compatible with the complementary metal oxide semiconductor (CMOS) manufacturing process. This improves the device’s reliability and stability and is favorable for achieving neuromorphic chips with higher integration density and low power consumption. This review summarizes CMOS-compatible neuromorphic devices and discusses their emulation of synaptic and neuronal functions as well as their applications in neuromorphic perception and computing. We highlight challenges and opportunities for further development of CMOS-compatible neuromorphic devices and systems.
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Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing. Owing to the unique advantages such as high parallelism and ultralow power consumption, bioinspired neuromorphic computing can have the capability of breaking through the bottlenecks of conventional computers and is now considered as an ideal option to realize the next-generation artificial intelligence. As the hardware carriers that allow the implementing of neuromorphic computing, neuromorphic devices are very critical in building neuromorphic chips. Meanwhile, the development of human visual systems and optogenetics also provides a new insight into how to study neuromorphic devices. The emerging optoelectronic neuromorphic devices feature the unique advantages of photonics and electronics, showing great potential in the neuromorphic computing field and attracting more and more attention of the scientists. In view of these, the main purpose of this review is to disclose the recent research advances in optoelectronic neuromorphic devices and the prospects of their practical applications. We first review the artificial optoelectronic synapses and neurons, including device structural features, working mechanisms, and neuromorphic simulation functions. Then, we introduce the applications of optoelectronic neuromorphic devices particularly suitable for the fields including artificial vision systems, artificial perception systems, and neuromorphic computing. Finally, we summarize the challenges to the optoelectronic neuromorphic devices, which we are facing now, and present some perspectives about their development directions in the future.
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