Brain-inspired Computing Research Articles

Beyond Memristors: Neuromorphic Computing Using Meminductors.

Resistors with memory (memristors), inductors with memory (meminductors) and capacitors with memory (memcapacitors) play different roles in novel computing architectures. We found that a coil with a magnetic core is an inductor with memory (meminductor) in terms of its inductance L(q) being a function of charge q. The history of the current passing through the coil is remembered by the magnetization inside the magnetic core. Such a meminductor can play a unique role (that cannot be played by a memristor) in neuromorphic computing, deep learning and brain-inspired computers since the time constant (t0=LC) of a neuromorphic RLC circuit is jointly determined by the inductance L and capacitance C, rather than the resistance R. As an experimental verification, this newly invented meminductor was used to reproduce the observed biological behavior of amoebae (the memorizing, timing and anticipating mechanisms). In conclusion, a beyond-memristor computing paradigm is theoretically sensible and experimentally practical.

Improvement of polarization switching in ferroelectric transistor by interface trap reduction for brain-inspired artificial synapses

Numerous devices have been studied to accomplish a brain-inspired neuromorphic computing system; however, their characteristics require improvement to enhance operation accuracy of neuromorphic systems. Here, we propose a ferroelectric field-effect transistor based on Hf0.5Zr0.5O2 for a high-performance artificial synaptic device by reducing interface trap density. Using polarization in the Hf0.5Zr0.5O2 ferroelectric layer, threshold voltage is precisely controlled to achieve synaptic characteristics. However, synaptic characteristics are degraded because interface traps cause threshold voltage shift in the direction opposite to the threshold voltage shift by polarization. Therefore, oxygen plasma treatment was performed to minimize degradation by interface traps. Thus, outstanding synaptic characteristics including the linearity of the weight update, the max/min weight ratio, and the number of weights were observed. Furthermore, high repeatability and long-term plasticity was observed at low operating power. Therefore, our study demonstrated a promising method for implementing a high-accuracy, low-power neuromorphic system with progressed artificial synaptic devices.

Uniform resistive switching and highly stable synaptic characteristics of HfOx sandwiched TaOx-based memristor for neuromorphic system

Emerging nanoscale devices, including memristors, have been extensively studied to implement biological synaptic functions such as learning and plasticity, which are the fundamental building blocks of brain-inspired neuromorphic computing. The memristor with analog switching ability exhibits linear tuning of weight during neural network training is a desirable synaptic device behavior. The importance of inserting a HfOx sandwiched layer in a TaOx/HfOx/TaOx memristor is to achieve analog set/reset operation along with improved spatial/temporal switching uniformity. The optimal resistive switching (RS) behavior can be attributed to asymmetric oxygen vacancy distribution in the stacked structure leading to the formation of an hourglass-shaped conductive filament. Furthermore, confining filament formation/rapture in the narrow fixed region displays superior endurance characteristics (dc cycles >2000 and ac cycles > 106) and uniform resistive switching with the set (reset) voltage variation constrained to 1.8 (2.9) %. Paired-pulse facilitation (PPF), a form of short-term synaptic plasticity is stimulated to replicate bio-synapse behavior. The stable long-term potentiation (LTP) and depression (LTD) behavior for more than 1000 epochs (>105 pulses) with excellent symmetry and linearity is achieved with 50 ns voltage pulse stimulation. The pattern recognition accuracy of 93% was achieved for an image of size 10 × 10 pixels after 13 epochs by deploying 100 synapses in Hopfield Neural Network (HNN) simulation. This comprehensive study demonstrates that the HfOx-inserted TaOx memristor has tremendous potential for application in future neuromorphic computing.

Reconfigurable 2D-ferroelectric platform for neuromorphic computing

To meet the requirement of data-intensive computing in the data-explosive era, brain-inspired neuromorphic computing have been widely investigated for the last decade. However, incompatible preparation processes severely hinder the cointegration of synaptic and neuronal devices in a single chip, which limited the energy-efficiency and scalability. Therefore, developing a reconfigurable device including synaptic and neuronal functions in a single chip with same homotypic materials and structures is highly desired. Based on the room-temperature out-of-plane and in-plane intercorrelated polarization effect of 2D α-In2Se3, we designed a reconfigurable hardware platform, which can switch from continuously modulated conductance for emulating synapse to spiking behavior for mimicking neuron. More crucially, we demonstrate the application of such proof-of-concept reconfigurable 2D ferroelectric devices on a spiking neural network with an accuracy of 95.8% and self-adaptive grow-when required network with an accuracy of 85% by dynamically shrinking its nodes by 72%, which exhibits more powerful learning ability and efficiency than the static neural network.

Artificial HfO2/TiOx Synapses with Controllable Memory Window and High Uniformity for Brain-Inspired Computing.

Artificial neural networks, as a game-changer to break up the bottleneck of classical von Neumann architectures, have attracted great interest recently. As a unit of artificial neural networks, memristive devices play a key role due to their similarity to biological synapses in structure, dynamics, and electrical behaviors. To achieve highly accurate neuromorphic computing, memristive devices with a controllable memory window and high uniformity are vitally important. Here, we first report that the controllable memory window of an HfO2/TiOx memristive device can be obtained by tuning the thickness ratio of the sublayer. It was found the memory window increased with decreases in the thickness ratio of HfO2 and TiOx. Notably, the coefficients of variation of the high-resistance state and the low-resistance state of the nanocrystalline HfO2/TiOx memristor were reduced by 74% and 86% compared with the as-deposited HfO2/TiOx memristor. The position of the conductive pathway could be localized by the nanocrystalline HfO2 and TiO2 dot, leading to a substantial improvement in the switching uniformity. The nanocrystalline HfO2/TiOx memristive device showed stable, controllable biological functions, including long-term potentiation, long-term depression, and spike-time-dependent plasticity, as well as the visual learning capability, displaying the great potential application for neuromorphic computing in brain-inspired intelligent systems.

Oxide Memristors for Brain-inspired Computing

Oxide Memristors for Brain-inspired Computing

Synaptic Characteristic of Hafnia-Based Ferroelectric Tunnel Junction Device for Neuromorphic Computing Application.

Owing to the 4th Industrial Revolution, the amount of unstructured data, such as voice and video data, is rapidly increasing. Brain-inspired neuromorphic computing is a new computing method that can efficiently and parallelly process rapidly increasing data. Among artificial neural networks that mimic the structure of the brain, the spiking neural network (SNN) is a network that imitates the information-processing method of biological neural networks. Recently, memristors have attracted attention as synaptic devices for neuromorphic computing systems. Among them, the ferroelectric doped-HfO2-based ferroelectric tunnel junction (FTJ) is considered as a strong candidate for synaptic devices due to its advantages, such as complementary metal-oxide-semiconductor device/process compatibility, a simple two-terminal structure, and low power consumption. However, research on the spiking operations of FTJ devices for SNN applications is lacking. In this study, the implementation of long-term depression and potentiation as the spike timing-dependent plasticity (STDP) rule in the FTJ device was successful. Based on the measured data, a CrossSim simulator was used to simulate the classification of handwriting images. With a high accuracy of 95.79% for the Mixed National Institute of Standards and Technology (MNIST) dataset, the simulation results demonstrate that our device is capable of differentiating between handwritten images. This suggests that our FTJ device can be used as a synaptic device for implementing an SNN.

Ultralow-Power Vertical Transistors for Multilevel Decoding Modes.

Organic field-effect transistors with parallel transmission and learning functions are of interest in the development of brain-inspired neuromorphic computing. However, the poor performance and high power consumption are the two main issues limiting their practical applications. Herein, an ultralow-power vertical transistor is demonstrated based on transition-metal carbides/nitrides (MXene) and organic single crystal. The transistor exhibits a high JON of 16.6mA cm-2 and a high JON /JOFF ratio of 9.12 × 105 under an ultralow working voltage of -1mV. Furthermore, it can successfully simulate the functions of biological synapse under electrical modulation along with consuming only 8.7 aJ of power per spike. It also permits multilevel information decoding modes with a significant gap between the readable time of professionals and nonprofessionals, producing a high signal-to-noise ratio up to 114.15dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.

Input-driven chaotic dynamics in vortex spin-torque oscillator

A new research topic in spintronics relating to the operation principles of brain-inspired computing is input-driven magnetization dynamics in nanomagnet. In this paper, the magnetization dynamics in a vortex spin-torque oscillator driven by a series of random magnetic field are studied through a numerical simulation of the Thiele equation. It is found that input-driven synchronization occurs in the weak perturbation limit, as found recently. As well, chaotic behavior is newly found to occur in the vortex core dynamics for a wide range of parameters, where synchronized behavior is disrupted by an intermittency. Ordered and chaotic dynamical phases are examined by evaluating the Lyapunov exponent. The relation between the dynamical phase and the computational capability of physical reservoir computing is also studied.

Enhanced Synaptic Features of ZnO/TaOx Bilayer Invisible Memristor for Brain-Inspired Computing

In this letter, we present a transparent bilayer ZnO/TaOx memristive synapse for brain-inspired computing. The device shows excellent AC endurance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{{9}}$ </tex-math></inline-formula> cycles) and high-temperature retention ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{{4}}$ </tex-math></inline-formula> s) without any degradation at 100 °C. The device exhibits highly stable repetitive 5130 potentiation (P) and depression (D) epochs with 1.026 M pulses. The multilevel characteristics (MLC) of the device are achieved by changing the pulse height from 0.7 to 1.1 V for long-term potentiation (LTP) and from −0.9 to −1.3 V for long-term depression (LTD) having gradual conductance change for both P and D cases. The synaptic features such as paired-pulse facilitation (PPF) and spike time-dependent plasticity (STDP) are measured using consecutive AC pulses. These unique features confirm that the synaptic device has excellent capability for the application in the brain-inspired computing systems.

Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing (Adv. Electron. Mater. 12/2022)

Neuromorphic Computing This frontispiece imagines the use of the studied devices in a neural network crossbar array for brain-inspired computing. The devices function as artificial synapses at each crosspoint and as artificial neurons at the input and output. The glowing lines show the magnetic domain wall positions, compared to the orange magnetic tunnel junctions. More details can be found in article number 2200563 by Christopher H. Bennett, Jean Anne C. Incorvia, and co-workers.

Complex Oxides for Brain-Inspired Computing: A Review.

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Optical synaptic devices with ultra-low power consumption for neuromorphic computing

Brain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system. In this study, we present a BP/CdS heterostructure-based artificial photonic synapse with ultra-low power consumption. The device shows remarkable negative light response with maximum responsivity up to 4.1 × 108 A W−1 at VD = 0.5 V and light power intensity of 0.16 μW cm−2 (1.78 × 108 A W−1 on average), which further enables artificial synaptic applications with average power consumption as low as 4.78 fJ for each training process, representing the lowest among the reported results. Finally, a fully-connected optoelectronic neural network (FONN) is simulated with maximum image recognition accuracy up to 94.1%. This study provides new concept towards the designing of energy-efficient artificial photonic synapse and shows great potential in high-performance neuromorphic vision systems.

Transient Mesoscopic Immiscibility, Viscosity Anomaly, and High Internal Pressure at the Semiconductor–Metal Transition in Liquid Ga2Te3

Gallium tellurides appear to be promising phase-change materials (PCMs) of the next generation for brain-inspired computing and reconfigurable optical metasurfaces. They are different from the benchmark PCMs because of sp3 gallium hybridization in both cubic Ga2Te3 and amorphous pulsed laser deposition (PLD) films. Liquid Ga2Te3 also shows a viscosity η(T) anomaly just above melting when η(T) first increases and only then starts decreasing. We used high-energy X-ray diffraction to observe a transient mesoscopic immiscibility that suggested dense metallic liquid droplets in a semiconducting melt. The η(T) shape was consistent with this finding. A vanishing first sharp diffraction peak that also shifts to a higher Q indicates a high internal pressure in the metallic melt, which produces a remarkable asymmetry of the Ga-Te nearest neighbor distances and is reminiscent of high-pressure rhombohedral Ga2Te3. The observed phenomena provide a realistic scenario for a fast, multilevel SET-RESET response, which also unravels similar trends in the purported density-driven liquid polyamorphism of water, phosphorus, sulfur, and other materials.

Nanostructured CuAlO2@ZnO optoelectronic device for artificial synaptic applications

Developing a neuromorphic system beyond conventional von Neumann’s architecture is necessary for future artificial intelligence applications and data-intensive computation. Optoelectronic synapses, like biological synapses, are extremely comparable in structure and operating mechanism and can learn directly from the light stimulus, making them a suitable technology for brain-inspired neuromorphic computing. Here, by constructing a core–shell structure of CuAlO2@ZnO oxide heterojunction, we have successfully fabricated the optoelectronic synaptic devices structured as ITO/CuAlO2@ZnO/ITO-glass that can be effectively motivated by light signal at visual light range. Moreover, there is an ultra-high gain of photocurrent generated in the device, resulting in an excitatory postsynaptic current (EPSC) of µA level even at a lower light intensity. In addition, the device could mimic multiple synaptic plasticities at the micro-level such as EPSC, paired-pulse facilitation (PPF), short-term plasticity (STP), spike-number-dependent plasticity (SNDP), and “advanced behaviors” of human-like innate learning and memory functions. The results presented here may serve as a new benchmark for the use of optoelectronic synaptic devices in artificial intelligence.

(Digital Presentation) Resistive Switching and Brain-Inspired Computing in Perovskite Nanowires and Quantum Wires

In the past decade, halide perovskites (HPs) have shot to fame in the genre of optoelectronics and photovoltaics owing to their large absorption co-effcient, high color purity, tunable bandgap and long charge diffusion lengths. Besides these traits, HPs also possess innumerable charge transport pathways, inherent hysteresis, high charge-carrier and ionic mobilities which render them as ideal candidates for resistive random access switching memories (RRAMs). However owing to material and electrical instability associated with HP thin-film devices, the figures-of-merits (FOMs) namely retention, endurance and switching speed were not up to the state of-the-art standard until recently. In order to revolutionize HP Re-RAMs we devised a unique device structure where we replaced the thin-film architecture with vertically aligned high density HP nanowires and quantum wires embedded in a porous alumina membrane (PAM) sandwiched between metallic silver and aluminum contacts. The excellent passivation provided by the PAM imparted the requisite electrical and material stability to the environmentally delicate HPs by drastically reducing the surface diffusion pathways and thereby thwarting the moisture induced attacks. Extrapolated retention time as high as 28.3 years and measured device endurance of a million cycles were obtained. Utilizing the single crystalline HP nanowires and quantum wires and their associated high ionic and electronic mobilities, switching speed as fast as 100 ps was also obtained. These FOMs represent record values for HP RRAMs ever reported. Furthermore a 14 nm lateral size HP quantum wire RRAM cell was fabricated and a cross-bar device architecture with a unique sneaky path mitigation scheme were developed, which successfully exhibited the scalability potential of our devices. We further coupled the optoelectronic and switching behaviors and were able to obtain optical programmability among the low resistance states. Besides data storage, the HP nanowires and quantum wires were employed in developing neuromorphic devices enabled with low power and high precision computing capabilities. Specifically, we obtained robust multi-level states in two types of brain-inspired devices capable of performing analog processing tasks by using silver as the top electrode and precisely controlling the current injection in the monocrytalline switching medium and by using indium doped tin oxide as the top electrode and inducing a novel valence change mechanism in the HP nanowires triggering the gradual conductance change. All in all, our nanowire and quantum wire devices propel HP RRAMs to the state-of-the-art standard in multifarious applications concerning future data storage and neuromorphic computing.

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Biologically-inspired neuromorphic computing paradigms are computational platforms that imitate synaptic and neuronal activities in the human brain to process big data flows in an efficient and cognitive manner. In the past decades, neuromorphic computing has been widely investigated in various application fields such as language translation, image recognition, modeling of phase, and speech recognition, especially in neural networks (NNs) by utilizing emerging nanotechnologies; due to their inherent miniaturization with low power cost, they can alleviate the technical barriers of neuromorphic computing by exploiting traditional silicon technology in practical applications. In this work, we review recent advances in the development of brain-inspired computing (BIC) systems with respect to the perspective of a system designer, from the device technology level and circuit level up to the architecture and system levels. In particular, we sort out the NN architecture determined by the data structures centered on big data flows in application scenarios. Finally, the interactions between the system level with the architecture level and circuit/device level are discussed. Consequently, this review can serve the future development and opportunities of the BIC system design.

Non-periodic input-driven magnetization dynamics in voltage-controlled parametric oscillator

Input-driven dynamical systems have attracted attention because their dynamics can be used as resources for brain-inspired computing. The recent achievement of human-voice recognition by spintronic oscillator also utilizes an input-driven magnetization dynamics. Here, we investigate an excitation of input-driven chaos in magnetization dynamics by voltage controlled magnetic anisotropy effect. The study focuses on the parametric magnetization oscillation induced by a microwave voltage and investigates the effect of random-pulse input on the oscillation behavior. Solving the Landau–Lifshitz–Gilbert equation, temporal dynamics of the magnetization and its statistical character are evaluated. In a weak perturbation limit, the temporal dynamics of the magnetization are mainly determined by the input signal, which is classified as input-driven synchronization. In a large perturbation limit, on the other hand, chaotic dynamics are observed, where the dynamical response is sensitive to the initial state. The existence of chaos is also identified by the evaluation of the Lyapunov exponent.

Towards a New Paradigm for Brain-inspired Computer Vision

Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques. However, its progress so far is not impressing. We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision, i.e., the biological vision is targeted for processing spatio-temporal patterns. Recently, a new paradigm for developing brain-inspired computer vision is emerging, which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data. In this paper, we review some recent primary works towards this new paradigm, including the development of spike cameras which acquire spiking signals directly from visual scenes, and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns, including models for object detection, tracking, and recognition. We also discuss about the future directions to improve the paradigm.

CerebelluMorphic: Large-Scale Neuromorphic Model and Architecture for Supervised Motor Learning.

The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.