A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors.
TL;DR: This work presents the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long- term depression (LTD) and spike timing dependent plasticity (STDP) behaviors.
Abstract: Physically transient electronic devices that can disappear on demand have great application prospects in the field of information security, implantable biomedical systems, and environment friendly electronics. On the other hand, the memristor-based artificial synapse is a promising candidate for new generation neuromorphic computing systems in artificial intelligence applications. Therefore, a physically transient synapse based on memristors is highly desirable for security neuromorphic computing and bio-integrated systems. Here, this is the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long-term depression (LTD) and spike timing dependent plasticity (STDP) behaviors. Moreover, to emulate the apoptotic process of biological neurons, the transient synapse devices can be dissolved completely in phosphate-buffered saline solution (PBS) or deionized (DI) water in 7 min. This work opens the route to security neuromorphic computing for smart security and defense electronic systems, as well as for neuro-medicine and implantable electronic systems.
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TL;DR: The progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics, and future research directions toward wearable artificial neuromorphic systems are suggested.
Abstract: Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.
226 citations
TL;DR: In this article, the potential applications, physical mechanism and critical issues of biomemristors as the next generation bioelectronics for information processing and human-machine interaction are discussed, which may inspire future development for new types of bioelectronic devices.
100 citations
TL;DR: A novel neuromorphic-photoelectric device of vertical van der Waals heterojunction phototransistors based on a colloidal 0D-CsPbBr3 -quantum-dots/2D-MoS2 heteroj junction channel is proposed using a polymer ion gel electrolyte as the gate dielectric and the results suggest that the proposed device has potential for applications associated with next-generation brain-like photoelectronic human-computer interactions and cognitive systems.
Abstract: Optoelectronic-neuromorphic transistors are vital for next-generation nanoscale brain-like computational systems. However, the hardware implementation of optoelectronic-neuromorphic devices, which are based on conventional transistor architecture, faces serious challenges with respect to the synchronous processing of photoelectric information. This is because mono-semiconductor material cannot absorb adequate light to ensure efficient light-matter interactions. In this work, a novel neuromorphic-photoelectric device of vertical van der Waals heterojunction phototransistors based on a colloidal 0D-CsPbBr3 -quantum-dots/2D-MoS2 heterojunction channel is proposed using a polymer ion gel electrolyte as the gate dielectric. A highly efficient photocarrier transport interface is established by introducing colloidal perovskite quantum dots with excellent light absorption capabilities on the 2D-layered MoS2 semiconductor with strong carrier transport abilities. The device exhibits not only high photoresponsivity but also fundamental synaptic characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, dynamic temporal filter, and light-tunable synaptic plasticity. More importantly, efficiency-adjustable photoelectronic Pavlovian conditioning and photoelectronic hybrid neuronal coding behaviors can be successfully implemented using the optical and electrical synergy approach. The results suggest that the proposed device has potential for applications associated with next-generation brain-like photoelectronic human-computer interactions and cognitive systems.
73 citations
TL;DR: This work reviews the recent advances in the development of photonic synapses and purely photonicsynapses and photoelectric synapses are described.
44 citations
01 May 2019
TL;DR: This theoretical system presents a quantum leap in terms of size, power consumption, and speed to both prosthetic human eyes and robotic vision in artificial intelligence‐based platforms such as autonomous vehicles.
Abstract: The state‐of‐the‐art conception of a bionic/robotic eye is a somewhat bulky multipart system comprising a video camera connected to a processing unit that in turn communicates data through a wireless transmitter to either an in vivo retinal implant or a computer system. An artificial cogni‐retina is a millimeter‐scale, intelligent apparatus designed as a replacement for these systems, while executing simple image processing tasks. As a bionic limb, it can connect directly to the optic nerve and perform rudimentary cognitive functions such as perceiving, learning, remembering, and classifying elementary visual data. This theoretical system presents a quantum leap in terms of size, power consumption, and speed to both prosthetic human eyes and robotic vision in artificial intelligence‐based platforms such as autonomous vehicles. Recently, an increasing number of publications have used interesting materials in artificial synaptic devices that drive this idea closer toward becoming a real‐world application. Such devices may form a basis for hardware‐based deep learning artificial neural networks that can potentially execute image processing tasks within a single clock cycle compared to software algorithms running on conventional von Neumann machines that require millions of cycles to perform image sensor interfacing, memory fetch operations, and data path propagation.
34 citations
References
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01 Jan 2018
TL;DR: The state of the art in memristor-based electronics is evaluated and the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing is explored.
Abstract: A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.
1,231 citations
TL;DR: This work describes an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors, opening a path towards extreme interconnectivity comparable to the human brain.
Abstract: A neuromorphic device based on the stable electrochemical fine-tuning of the conductivity of an organic ionic/electronic conductor is realized. These devices show high linearity, low noise and extremely low switching voltage. The brain is capable of massively parallel information processing while consuming only ∼1–100 fJ per synaptic event1,2. Inspired by the efficiency of the brain, CMOS-based neural architectures3 and memristors4,5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy ( 500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems6,7. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.
1,119 citations
TL;DR: A set of materials, manufacturing schemes, device components, and theoretical design tools for a silicon-based complementary metal oxide semiconductor (CMOS) technology that has this type of transient behavior are reported, together with integrated sensors, actuators, power supply systems, and wireless control strategies.
Abstract: A remarkable feature of modern silicon electronics is its ability to remain physically invariant, almost indefinitely for practical purposes. Although this characteristic is a hallmark of applications of integrated circuits that exist today, there might be opportunities for systems that offer the opposite behavior, such as implantable devices that function for medically useful time frames but then completely disappear via resorption by the body. We report a set of materials, manufacturing schemes, device components, and theoretical design tools for a silicon-based complementary metal oxide semiconductor (CMOS) technology that has this type of transient behavior, together with integrated sensors, actuators, power supply systems, and wireless control strategies. An implantable transient device that acts as a programmable nonantibiotic bacteriocide provides a system-level example.
1,026 citations
08 Feb 2018
TL;DR: It is shown that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance.
Abstract: Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification.
733 citations
TL;DR: The seamless integration of a memristor and transistor into one multi-terminal device could enable complex neuromorphic learning and the study of the physics of defect kinetics in two-dimensional materials.
Abstract: Memristors are two-terminal passive circuit elements that have been developed for use in non-volatile resistive random-access memory and may also be useful in neuromorphic computing. Memristors have higher endurance and faster read/write times than flash memory and can provide multi-bit data storage. However, although two-terminal memristors have demonstrated capacity for basic neural functions, synapses in the human brain outnumber neurons by more than a thousandfold, which implies that multi-terminal memristors are needed to perform complex functions such as heterosynaptic plasticity. Previous attempts to move beyond two-terminal memristors, such as the three-terminal Widrow-Hoff memristor and field-effect transistors with nanoionic gates or floating gates, did not achieve memristive switching in the transistor. Here we report the experimental realization of a multi-terminal hybrid memristor and transistor (that is, a memtransistor) using polycrystalline monolayer molybdenum disulfide (MoS2) in a scalable fabrication process. The two-dimensional MoS2 memtransistors show gate tunability in individual resistance states by four orders of magnitude, as well as large switching ratios, high cycling endurance and long-term retention of states. In addition to conventional neural learning behaviour of long-term potentiation/depression, six-terminal MoS2 memtransistors have gate-tunable heterosynaptic functionality, which is not achievable using two-terminal memristors. For example, the conductance between a pair of floating electrodes (pre- and post-synaptic neurons) is varied by a factor of about ten by applying voltage pulses to modulatory terminals. In situ scanning probe microscopy, cryogenic charge transport measurements and device modelling reveal that the bias-induced motion of MoS2 defects drives resistive switching by dynamically varying Schottky barrier heights. Overall, the seamless integration of a memristor and transistor into one multi-terminal device could enable complex neuromorphic learning and the study of the physics of defect kinetics in two-dimensional materials.
628 citations