Neuromorphic Electronics Research Articles

Multiplexed Neurotransmission Emulated by a p–n Cross Nanowire Synaptic Transistor for Satiety, Depression, and Drug Withdrawal

AbstractThis paper presents the first synaptic transistor (ST) that combines crossed p‐type and n‐type semiconductor nanowires (NWs) to form a bipolar conductive channels. The bipolar channel with both electron and hole transport paths enables the emulation of multiplexed neurotransmission of different neurotransmitters in a brain to realize complex neural functions using nanowires. Except for basic synaptic functions, the device demonstrates the potential to distinguish reward‐processing and aversive responses. The opposite polarities of NWs enable emulation of a series of complex neural activities using a single electronic device, such as satiety, depression, and drug withdrawal. The device is applicable to deep convolutional neural networks for electrocardiograms. The new strategy of versatile use of electro‐hydrodynamically printed NWs (e‐NWs) is expected to facilitate emulation of complex neural behaviors, and the realization of future neuromorphic electronics that are capable of these behaviors.

On Local Activity and Edge of Chaos in a NaMLab Memristor.

Local activity is the capability of a system to amplify infinitesimal fluctuations in energy. Complex phenomena, including the generation of action potentials in neuronal axon membranes, may never emerge in an open system unless some of its constitutive elements operate in a locally active regime. As a result, the recent discovery of solid-state volatile memory devices, which, biased through appropriate DC sources, may enter a local activity domain, and, most importantly, the associated stable yet excitable sub-domain, referred to as edge of chaos, which is where the seed of complexity is actually planted, is of great appeal to the neuromorphic engineering community. This paper applies fundamentals from the theory of local activity to an accurate model of a niobium oxide volatile resistance switching memory to derive the conditions necessary to bias the device in the local activity regime. This allows to partition the entire design parameter space into three domains, where the threshold switch is locally passive (LP), locally active but unstable, and both locally active and stable, respectively. The final part of the article is devoted to point out the extent by which the response of the volatile memristor to quasi-static excitations may differ from its dynamics under DC stress. Reporting experimental measurements, which validate the theoretical predictions, this work clearly demonstrates how invaluable is non-linear system theory for the acquirement of a comprehensive picture of the dynamics of highly non-linear devices, which is an essential prerequisite for a conscious and systematic approach to the design of robust neuromorphic electronics. Given that, as recently proved, the potassium and sodium ion channels in biological axon membranes are locally active memristors, the physical realization of novel artificial neural networks, capable to reproduce the functionalities of the human brain more closely than state-of-the-art purely CMOS hardware architectures, should not leave aside the adoption of resistance switching memories, which, under the appropriate provision of energy, are capable to amplify the small signal, such as the niobium dioxide micro-scale device from NaMLab, chosen as object of theoretical and experimental study in this work.

Nitrogen-induced ultralow power switching in flexible ZnO-based memristor for artificial synaptic learning

An energy-efficient memristive synapse is highly desired for the development of brain-like neurosynaptic chips. In this work, a ZnO-based memristive synapse with ultralow-power consumption was achieved by simple N-doping. The introduction of N atoms, as the acceptor, reduces the carrier concentration and greatly increases the resistance of the ZnO film. The low energy consumption, which is as low as 60 fJ per synaptic event, can be achieved in our device. Essential synaptic learning functions have been demonstrated, including excitatory postsynaptic current, paired-pulse facilitation, and experience-dependent learning behaviors. Furthermore, the device can still exhibit the synaptic performance in the bent state or even after 100 bending cycles. Our memristive synapse is not only promising for energy-efficient neuromorphic computing systems but also suitable for the development of wearable neuromorphic electronics.

Multimodal optoelectronic neuromorphic electronics based on lead-free perovskite-mixed carbon nanotubes

It is of great significance to develop multifunction-integrated photoelectric synaptic devices with the simple structure to imitate the functions of eyes. In this work, we reported the multimodal photoelectric neuromorphic thin film transistors (TFTs) with single gate and two input terminals using sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) mixed with lead-free perovskite (CsBi3I10) and lightly n-doped silicon as active layers and gate electrodes. The results showed that optical and electrical responses of SWCNT TFTs could increase 26 and 23 times after addition of CsBi3I10 under the same pulse sequence stimulation, respectively. Apart from emulating a series of typical synaptic functionalities, an artificial neural network based on SWCNT phototransistors was simulated to be trained for the recognition of handwritten digits in the Modified National Institute of Standards and Technology with the maximum accuracy of about 85.46%. Moreover, our synaptic devices can imitate traditional Pavlovian conditioning, and show NOR logic, reproducible flash memory functions under the optical and electrical programming stimulations. It is noted that the erasing voltage (3 V, 0.2 s) is one of the best value among the reported phototransistor memory. Furthermore, our synaptic devices can work well after more than 150 programming/erasing operation cycles and over 40 days testing.

Organic electronic synapses with low energy consumption

<h2>Summary</h2> The von Neumann computing architecture consists of separated processing and memory elements; it is too bulky and energy-intensive to be implemented in the upcoming artificial intelligence age. In contrast, neurons and synapses in a brain perform learning and memory in an integrated manner and function energy-efficiently by analog adjustment of synaptic strengths in response to stimulation. Organic artificial synapses provide good emulation of the functions and structures of biological synapses and are easily fabricated and therefore can be applied to various neuromorphic electronic devices. In particular, organic artificial synapses that consume energy at a level comparable to that of a biological synapse show great promise for use in future low-energy neuromorphic devices. Here, we review the trends of energy consumption of organic artificial synapses and how it is affected by the structure, materials, and operation mechanism. We also present a strategy to decrease the energy consumption of organic neuromorphic devices. Our review will help the development of versatile low-energy organic neuromorphic electronics.

Recent Progress in Transistor‐Based Optoelectronic Synapses: From Neuromorphic Computing to Artificial Sensory System

Neuromorphic electronics draw attention as innovative approaches that facilitate hardware implementation of next‐generation artificial intelligent system including neuromorphic in‐memory computing, artificial sensory perception, and humanoid robotics. Among the various neuromorphic devices, optoelectronic synapses are promising neuromorphic devices that use optical means to mimic synaptic plasticity and related functions. Compared with classical neuromorphic chip based on electronic synapses using electrical means, photonic neuromorphic chip using light as input spike signal can be attractive alternative approach for next‐generation artificial intelligent system capable of high density, low power consumption, and low crosstalk. Thus, various optoelectronic synaptic electronics have been developed to overcome the drawback of conventional artificial intelligent system based on electrical synapses. Herein, the recent progresses in transistor‐based optoelectronic synapses for artificial intelligent system and review their device architecture, neuromorphic operational mechanisms, manufacturing methodologies, and advanced applications for artificial intelligent computing and visual perception systems are focused. Finally, the future challenges and research direction in the optoelectronic synaptic research are discussed.

Photonics for artificial intelligence and neuromorphic computing

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices.

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Thin-film transistors for emerging neuromorphic electronics: fundamentals, materials, and pattern recognition

This review paper provides an overview of the recent successful simulation of pattern recognition with TFT-based artificial synapses from device- to system-level.

Synaptic transistors with human brain-like fJ energy consumption via double oxide semiconductor engineering for neuromorphic electronics

This study proposes double oxide semiconductors as a strategy for human-brain like energy consuming synaptic transistor. IZO/IGZO synaptic transistor consumed a minimum energy of 0.269 fJ, and successfully mimicked various synaptic plasticity.

Flexible electro-optical neuromorphic transistors with tunable synaptic plasticity and nociceptive behavior

An electro-optical modulation synaptic device is proposed; it can emulate both brain-like processing and nervous perception functions by using a special nanoparticle-based conductive channel for the first time. In an electrical-processing synapse (EPS), ion diffusion behaviors during point contact and plane contact showed short-term plasticity with different gains and various synaptic functions such as paired-pulse facilitation, memory enhancement, spatiotemporal signal processing, and fault-tolerant behavior of logic operations. The EPS array is also mechanically flexible; the postsynaptic current retained 41.8% of the initial value after 10,000 bends, which represents the largest number of bending cycles of a flexible synaptic transistor so far. As a nervous optical sensing-processing synapse (OSPS), it can respond to ultraviolet light of different intensities and durations under sensitive millivolt reading voltage and also act as nociceptors to realize the pain perception. The ability of synaptic device to adjust their responses is important to connect brain-like devices to an artificial nervous system, so this electro-optical synaptic unit is a promising candidate for use in neuromorphic and flexible electronics, to combine brain-like processing and artificial sensory nerves.

Neuro-Transistor Based on UV-Treated Charge Trapping in MoTe2 for Artificial Synaptic Features.

The diversity of brain functions depend on the release of neurotransmitters in chemical synapses. The back gated three terminal field effect transistors (FETs) are auspicious candidates for the emulation of biological functions to recognize the proficient neuromorphic computing systems. In order to encourage the hysteresis loops, we treated the bottom side of MoTe2 flake with deep ultraviolet light in ambient conditions. Here, we modulate the short-term and long-term memory effects due to the trapping and de-trapping of electron events in few layers of a MoTe2 transistor. However, MoTe2 FETs are investigated to reveal the time constants of electron trapping/de-trapping while applying the gate-voltage pulses. Our devices exploit the hysteresis effect in the transfer curves of MoTe2 FETs to explore the excitatory/inhibitory post-synaptic currents (EPSC/IPSC), long-term potentiation (LTP), long-term depression (LTD), spike timing/amplitude-dependent plasticity (STDP/SADP), and paired pulse facilitation (PPF). Further, the time constants for potentiation and depression is found to be 0.6 and 0.9 s, respectively which seems plausible for biological synapses. In addition, the change of synaptic weight in MoTe2 conductance is found to be 41% at negative gate pulse and 38% for positive gate pulse, respectively. Our findings can provide an essential role in the advancement of smart neuromorphic electronics.

Aqueous solution processed mesoporous silica-gated photo-perception neuromorphic transistor

Our body is a multiple perception learning system. Bionic application of perception cognitive functions based on neuromorphic electronics would endow artificial intelligence new intension. In particular, electrolyte-gated transistors have been suggested for neuromorphic platform applications. Due to the fundamental limitations for ionic liquid and ionic gel-based electrolyte, it would be interesting to obtain solid-state electrolytes through a vacuum free system. Thus, in the present work, a simple aqueous solution-processed mesoporous silica coating-gated oxide photo-perception neuromorphic transistors have been proposed. The neuromorphic transistors exhibit good electrical performances. A resistor-loaded inverter is built, exhibiting typical inverter operation at low working voltage. ‘Selective potentiation and depression’ behaviors have been mimicked on the neuromorphic transistor. In addition, the proposed neuromorphic transistor shows optical and electrical synergic response behaviors. Neurological behavior of conditioned taste aversion learning has been mimicked. Thus, the single photo-perception neuromorphic transistor could integrate the functions of perception and neuromorphic algorithm. The proposed photo-perception neuromorphic transistors could have potentials in artificial perception learning systems.

Sub‐Femtojoule‐Energy‐Consumption Conformable Synaptic Transistors Based on Organic Single‐Crystalline Nanoribbons

AbstractInspired from powerful functionalities of human brain, artificial synapses are innovated continuously for the construction of brain‐like neuromorphic electronics. The quest to rival the ultralow energy consumption of biological synapses has become highly compelling, but remains extremely difficult due to the lack of appropriate materials and device construction. In this study, organic single‐crystalline nanoribbon active layer and elastic embedded photolithographic electrodes are first designed in synaptic transistors to reduce energy consumption of single device. The minimum energy consumption (0.29 fJ) of one synaptic event is far lower than that of biological synapse (10 fJ). Notably, sub‐femtojoule‐energy‐consumption synaptic transistors can simulate various biological plastic behaviors even under different tensile and compressive strains, offering a new guidance for the construction of ultralow‐energy‐consuming neuromorphic electronic devices and the development of flexible artificial intelligence electronics in the future.

Electronic structure and charge transport mechanism in a forming-free SiO x -based memristor

THe memristor is a key memory element for neuromorphic electronics and new generation flash memories. One of the most promising materials for memristor technology is silicon oxide SiO x , which is compatible with silicon-based technology. In this paper, the electronic structure and charge transport mechanism in a forming-free SiO x -based memristor fabricated with the plasma enhanced chemical vapor deposition method is investigated. The experimental current-voltage characteristics measured at different temperatures in high-resistance, low-resistance and intermediate states are compared with various charge transport theories. The charge transport in all states is limited by the space charge-limited current model. The trap parameters, responsible for the charge transport in a SiO x -based memristor in different states, are determined.

Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics.

The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low-power-switching capability, and hetero-integration compatibility. Hence, a large number of experimental demonstrations on 2D material-based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk-material-based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material-based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D-memristor-based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.

Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses

An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.

Introduction of Interfacial Load Polymeric Layer to Organic Flexible Memristor for Regulating Conductive Filament Growth

AbstractIn flexible neuromorphic electronics, solution‐processed organic memristors are important elements to perform memory functions. Despite considerable development for improving performances of organic memristors, the devices still exhibit the poor reliability and uniformity due to the stochastic characteristics of the conductive filament (CF) growth. Herein, the effective concept of introducing the interfacial load polymer (ILP) layers that control the CF growth in flexible organic memristors is demonstrated. In the flexible organic memristor, the ILP serves as an internal load resistor that regulates the CF growth in the electrolyte medium and the electron blocking layer, hence realizing self‐rectifying characteristics. In particular, the ILP provides the self‐compliance current of the device, which delicately limits the overgrowth of CFs. The flexible device delivers higher electrical performance (better reliability, uniformity, and the switching currents) than conventional devices without the ILP. Moreover, the device operates stably under repeated bending–straightening deformations. This unprecedented concept of achieving the capabilities of self‐compliance current and self‐rectifying property in a single memristor will provide a practical platform for constructing and realizing next‐generation flexible neuromorphic systems.

Multilevel Nonvolatile Memcapacitance in Graphene‐Silk Fibroin Biocomposite Paper

AbstractThe demand for flexible memory devices is growing due to the development of flexible neuromorphic electronics for wearable devices, soft robotics, and biologically‐inspired circuits. Described herein is a flexible memcapacitor based on a biocomposite paper composed of reduced graphene oxide (rGO) and silk fibroin (SF) via a gel‐film transformation process. The presented rGO‐SF/H3PO4‐polyvinyl alcohol (PVA)/rGO memory stack exhibits analog, reversible, and nonvolatile memcapacitance characteristics. Impressively, the stack has a broad range of high capacitance states so as to be tuned to at least four capacitive states by the operating voltages. This memcapacitive behavior is ascribed to an interfacial charge build‐up (capacitive double layer) modulated by the H+ migration, which is trapped/detrapped by the rGO‐SF paper depending on bias conditions. Further, the flexibility tests are conducted to confirm the applicability of the rGO‐SF/H3PO4‐PVA/rGO stack to flexible devices. Thus, the rGO‐SF papers can be used as electronic materials for capacitive memory in neuromorphic systems where operation at biocompatibility, low‐energy, and mechanical flexibility are important issues.

Artificially Intelligent Tactile Ferroelectric Skin.

Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human‐interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e‐skin) based on arrays of ferroelectric‐gate field‐effect transistors with dome‐shape tactile top‐gates, which can simultaneously sense and learn from a variety of tactile information, is introduced. To test the e‐skin, tactile pressure is applied to a dome‐shaped top‐gate that measures ferroelectric remnant polarization in a gate insulator. This results in analog conductance modulation that is dependent upon both the number and magnitude of input pressure‐spikes, thus mimicking diverse tactile and essential synaptic functions. Specifically, the device exhibits excellent cycling stability between long‐term potentiation and depression over the course of 10 000 continuous input pulses. Additionally, it has a low variability of only 3.18%, resulting in high‐performance and robust tactile perception learning. The 4 × 4 device array is also able to recognize different handwritten patterns using 2‐dimensional spatial learning and recognition, and this is successfully demonstrated with a high degree accuracy of 99.66%, even after considering 10% noise.