Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions

In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.

Reconfigurable memlogic long wave infrared sensing with superconductors

Optical sensors with in-cell logic and memory capabilities offer new horizons in realizing machine vision beyond von Neumann architectures and have been attempted with two-dimensional materials, memristive oxides, phase-changing materials etc. Noting the unparalleled performance of superconductors with both quantum-limited optical sensitivities and ultra-wide spectrum coverage, here we report a superconducting memlogic long-wave infrared sensor based on the bistability in hysteretic superconductor-normal phase transition. Driven cooperatively by electrical and optical pulses, the device offers deterministic in-sensor switching between resistive and superconducting (hence dissipationless) states with persistence > 105 s. This results in a resilient reconfigurable memlogic system applicable for, e.g., encrypted communications. Besides, a high infrared sensitivity at 12.2 μm is achieved through its in-situ metamaterial perfect absorber design. Our work opens the avenue to realize all-in-one superconducting memlogic sensors, surpassing biological retina capabilities in both sensitivity and wavelength, and presents a groundbreaking opportunity to integrate visional perception capabilities into superconductor-based intelligent quantum machines.

Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions

Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ's applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system.

Spin-Valve-Controlled Triggering of Superconductivity

We have studied the proximity effect in an SF1S1F2s superconducting spin valve consisting of a massive superconducting electrode (S) and a multilayer structure formed by thin ferromagnetic (F1,2) and superconducting (S1, s) layers. Within the framework of the Usadel equations, we have shown that changing the mutual orientation of the magnetization vectors of the F1,2 layers from parallel to antiparallel serves to trigger superconductivity in the outer thin s-film. We studied the changes in the pair potential in the outer s-film and found the regions of parameters with a significant spin-valve effect. The strongest effect occurs in the region of parameters where the pair-potential sign is changed in the parallel state. This feature reveals new ways to design devices with highly tunable inductance and critical current.

Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing

Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

A bifunctional superconducting cell as flux qubit and neuron

Josephson digital or analog ancillary circuits are an essential part of a large number of modern quantum processors. The natural candidate for the basis of tuning, coupling, and neromorphic co-processing elements for processors based on flux qubits is the adiabatic (reversible) superconducting logic cell. Using the simplest implementation of such a cell as an example, we have investigated the conditions under which it can optionally operate as an auxiliary qubit while maintaining its "classical" neural functionality. The performance and temperature regime estimates obtained confirm the possibility of practical use of a single-contact inductively shunted interferometer in a quantum mode in adjustment circuits for q-processors.

Doping-modulated lateral asymmetric Schottky diode as a high-performance self-powered synaptic device

In the post-Moore era, the gradually saturated computational capability of conventional digital computers showing the opposite trend as the exponentially increasing data volumes imperatively required a platform or technology to break this bottleneck. Brain-inspired neuromorphic computing promises to inherently improve the efficiency of information processing and computation by means of the highly parallel hardware architecture to reduce global data transmission. Here, we demonstrate a compact device technology based on the barrier asymmetry to achieve zero-consumption self-powered synaptic devices. In order to tune the device behaviors, the typical chemical doping is used to tailor the asymmetry for energy harvesting. Finally, in our demonstrated devices, the open-circuit voltage (VOC) and power-conversion efficiency (PCE) can be modulated up to 0.77 V and 6%, respectively. Optimized photovoltaic features affords synaptic devices with an outstanding programming weight states, involving training facilitation, stimulus reinforce and consolidation. Based on self-powered system, this work further presents a highly available modulation scheme, which achieves excellent device behaviors while ensuring the zero-energy consumption.

Bio-Inspired Design of Superconducting Spiking Neuron and Synapse

The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.

Tunnel Josephson Junction with Spin-Orbit/Ferromagnetic Valve

We have theoretically studied the transport properties of the SIsNSOF structure consisting of thick (S) and thin (s) films of superconductor, an insulator layer (I), a thin film of normal metal with spin-orbit interaction (SOI) (NSO), and a monodomain ferromagnetic layer (F). The interplay between superconductivity, ferromagnetism, and spin-orbit interaction allows the critical current of this Josephson junction to be smoothly varied over a wide range by rotating the magnetization direction in the single F-layer. We have studied the amplitude of the spin valve effect and found the optimal ranges of parameters.

Bimodal ionic photomemristor based on a high-temperature oxide superconductor/semiconductor junction

Memristors, a cornerstone for neuromorphic electronics, respond to the history of electrical stimuli by varying their electrical resistance across a continuum of states. Much effort has been recently devoted to developing an analogous response to optical excitation. Here we realize a novel tunnelling photo-memristor whose behaviour is bimodal: its resistance is determined by the dual electrical-optical history. This is obtained in a device of ultimate simplicity: an interface between a high-temperature superconductor and a transparent semiconductor. The exploited mechanism is a reversible nanoscale redox reaction between both materials, whose oxygen content determines the electron tunnelling rate across their interface. The redox reaction is optically driven via an interplay between electrochemistry, photovoltaic effects and photo-assisted ion migration. Besides their fundamental interest, the unveiled electro-optic memory effects have considerable technological potential. Especially in combination with high-temperature superconductivity which, in addition to facilitating low-dissipation connectivity, brings photo-memristive effects to the realm of superconducting electronics.

A New Class of Cluster-Matrix Nanocomposite Made of Fully Miscible Components

Nanocomposite materials, consisting of two or more phases, at least one of which has a nanoscale dimension, play a distinctive role in materials science because of the multiple possibilities for tailoring their structural properties and, consequently, their functionalities. In addition to the challenges of controlling the size, size distribution, and volume fraction of nanometer phases, thermodynamic stability conditions limit the choice of constituent materials. This study goes beyond this limitation by showing the possibility of achieving nanocomposites from a bimetallic system, which exhibits complete miscibility under equilibrium conditions. A series of nanocomposite samples with different compositions are synthesized by the co-deposition of 2000-atom Ni-clusters and a flux of Cu-atoms using a novel cluster ion beam deposition system. The retention of the metastable nanostructure is ascertained from atom probe tomography (APT), magnetometry, and magnetotransport studies. APT confirms the presence of nanoscale regions with ≈100 at% Ni. Magnetometry and magnetotransport studies reveal superparamagnetic behavior and magnetoresistance stemming from the single-domain ferromagnetic Ni-clusters embedded in the Cu-matrix. Essentially, the magnetic properties of the nanocomposites can be tailored by the precise control of the Ni concentration. The initial results offer a promising direction for future research on nanocomposites consisting of fully miscible elements.

Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser

Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons in neuromorphic (brain-inspired) photonic systems. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) coupled to a photodetector as a receiver and a vertical cavity surface emitting laser as a transmitter. We demonstrate a well-defined excitability threshold, above which the neuron produces optical spiking responses with characteristic neural-like refractory period. We utilise its fan-in capability to perform in-device coincidence detection (logical AND) and exclusive logical OR (XOR) tasks. These results provide first experimental validation of deterministic triggering and tasks in an RTD-based spiking optoelectronic neuron with both input and output optical (I/O) terminals. Furthermore, we also investigate in simulation the prospects of the proposed system for nanophotonic implementation in a monolithic design combining a nanoscale RTD element and a nanolaser; therefore demonstrating the potential of integrated RTD-based excitable nodes for low footprint, high-speed optoelectronic spiking neurons in future neuromorphic photonic hardware.

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 J_ON of 16.6 mA cm^-2 and a high J_ON /J_OFF ratio of 9.12 × 10⁵ under an ultralow working voltage of -1 mV. 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 114.15 dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.

Superconducting Valve Exploiting Interplay between Spin-Orbit and Exchange Interactions

We theoretically investigated the proximity effect in SNSOF and SF'F structures consisting of a superconductor (S), a normal metal (NSO), and ferromagnetic (F',F) thin films with spin-orbit interaction (SOI) in the NSO layer. We show that a normal layer with spin-orbit interaction effectively suppresses triplet correlations generated in a ferromagnetic layer. Due to this effect, the critical temperature of the superconducting layer in the SNSOF multilayer turns out to be higher than in a similar multilayer without spin-orbit interaction in the N layer. Moreover, in the presence of a mixed type of spin-orbit interaction involving the Rashba and Dresselhaus components, the SNSOF structure is a spin valve, whose critical temperature is determined by the direction of the magnetization vector in the F layer. We calculated the control characteristics of the SNSOF spin valve and compared them with those available in traditional SF'F devices with two ferromagnetic layers. We concluded that SNSOF structures with one controlled F layer provide solid advantages over the broadly considered SF'F spin valves, paving the way for high-performance storage components for superconducting electronics.

Spintronic Integrate-Fire-Reset Neuron with Stochasticity for Neuromorphic Computing

Spintronics has been recently extended to neuromorphic computing because of its energy efficiency and scalability. However, a biorealistic spintronic neuron with probabilistic "spiking" and a spontaneous reset functionality has not been demonstrated yet. Here, we propose a biorealistic spintronic neuron device based on the heavy metal (HM)/ferromagnet (FM)/antiferromagnet (AFM) spin-orbit torque (SOT) heterostructure. The spintronic neuron can autoreset itself after firing due to the exchange bias of the AFM. The firing process is inherently stochastic because of the competition between the SOT and AFM pinning effects. We also implement a restricted Boltzmann machine (RBM) and stochastic integration multilayer perceptron (SI-MLP) using our proposed neuron. Despite the bit-width limitation, the proposed spintronic model can achieve an accuracy of 97.38% in pattern recognition, which is even higher than the baseline accuracy (96.47%). Our results offer a spintronic device solution to emulate biologically realistic spiking neurons.

Ultrafast signatures of magnetic inhomogeneity in Pd1- x Fe x (x ≤ 0.08) epitaxial thin films

A series of Pd_{1- x} Fe _x alloy epitaxial films (x = 0, 0.038, 0.062, and 0.080), a material promising for superconducting spintronics, was prepared and studied with ultrafast optical and magneto-optical laser spectroscopy in a wide temperature range of 4-300 K. It was found that the transition to the ferromagnetic state causes a qualitative change of both the reflectivity and the magneto-optical Kerr effect transients. A nanoscale magnetic inhomogeneity of the ferromagnet/paramagnet type inherent in the palladium-rich Pd_{1- x} Fe _x alloys reveals itself through the occurrence of a relatively slow, 10-25 ps, photoinduced demagnetization component following a subpicosecond one; the former vanishes at low temperatures only in the x = 0.080 sample. We argue that the 10 ps timescale demagnetization originates most probably from the diffusive transport of d electrons under the condition of nanoscale magnetic inhomogeneities. The low-temperature fraction of the residual paramagnetic phase can be deduced from the magnitude of the slow reflectivity relaxation component. It is estimated as ≈30% for x = 0.038 and ≈15% for x = 0.062 films. The minimal iron content ensuring the magnetic homogeneity of the ferromagnetic state in the Pd_{1- x} Fe _x alloy at low temperatures is about 7-8 atom %.

A superconducting adiabatic neuron in a quantum regime

We explore the dynamics of an adiabatic neural cell of a perceptron artificial neural network in a quantum regime. This mode of cell operation is assumed for a hybrid system of a classical neural network whose configuration is dynamically adjusted by a quantum co-processor. Analytical and numerical studies take into account non-adiabatic processes as well as dissipation, which leads to smoothing of quantum coherent oscillations. The obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function.

Tunable superconducting neurons for networks based on radial basis functions

The hardware implementation of signal microprocessors based on superconducting technologies seems relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron. Special attention is paid to tuning the activation functions to a Gaussian form with relatively large amplitude. For the practical implementation of the required tunability, we proposed and investigated heterostructures designed for the implementation of adjustable inductors that consist of superconducting, ferromagnetic, and normal layers.

Superconducting Bio-Inspired Au-Nanowire-Based Neurons

High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons—high-performance, energy-efficient, and compact elements of neuromorphic processor. The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored theoretically. A comparison with the Izhikevich mathematical model of biological neurons is carried out.

A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks

Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.

Building block 3D printing based on molecular self-assembly monolayer with self-healing properties

The spontaneous formation of biological substances, such as human organs, are governed by different stimuli driven by complex 3D self-organization protocols at the molecular level. The fundamentals of such molecular self-assembly processes are critical for fabrication of advanced technological components in nature. We propose and experimentally demonstrate a promising 3D printing method with self-healing property based on molecular self-assembly-monolayer principles, which is conceptually different than the existing 3D printing protocols. The proposed molecular building-block approach uses metal ion-mediated continuous self-assembly of organic molecular at liquid-liquid interfaces to create 2D and 3D structures. Using this technique, we directly printed nanosheets and 3D rods using dithiol molecules as building block units.

Perceptions of Justice By Algorithms

Artificial Intelligence and algorithms are increasingly able to replace human workers in cognitively sophisticated tasks, including ones related to justice. Many governments and international organizations are discussing policies related to the application of algorithmic judges in courts. In this paper, we investigate the public perceptions of algorithmic judges. Across two experiments (N = 1,822), and an internal meta-analysis (N = 3,039), our results show that even though court users acknowledge several advantages of algorithms (i.e., cost and speed), they trust human judges more and have greater intentions to go to the court when a human (vs. an algorithmic) judge adjudicates. Additionally, we demonstrate that the extent that individuals trust algorithmic and human judges depends on the nature of the case: trust for algorithmic judges is especially low when legal cases involve emotional complexities (vs. technically complex or uncomplicated cases).

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10506-022-09312-z.

Low-Energy-Consumption and Electret-Free Photosynaptic Transistor Utilizing Poly(3-hexylthiophene)-Based Conjugated Block Copolymers

Neuromorphic computation possesses the advantages of self-learning, highly parallel computation, and low energy consumption, and is of great promise to overcome the bottleneck of von Neumann computation. In this work, a series of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) with different coil segments, including polystyrene, poly(2-vinylpyridine) (P2VP), poly(2-vinylnaphthalene), and poly(butyl acrylate), are utilized in photosynaptic transistor to emulate paired-pulse facilitation, spike time/rate-dependent plasticity, short/long-term neuroplasticity, and learning-forgetting-relearning processes. P3HT serves as a carrier transport channel and a photogate, while the insulating coils with electrophilic groups are for charge trapping and preservation. Three main factors are unveiled to govern the properties of these P3HT-based BCPs: i) rigidity of the insulating coil, ii) energy levels between the constituent polymers, and iii) electrophilicity of the insulating coil. Accordingly, P3HT-b-P2VP-based photosynaptic transistor with a sought-after BCP combination demonstrates long-term memory behavior with current contrast up to 10⁵ , short-term memory behavior with high paired-pulse facilitation ratio of 1.38, and an ultralow energy consumption of 0.56 fJ at an operating voltage of -0.0003 V. As far as it is known, this is the first work to utilize conjugated BCPs in an electret-free photosynaptic transistor showing great potential to the artificial intelligence technology.

BrainFreeze: Expanding the Capabilities of Neuromorphic Systems Using Mixed-Signal Superconducting Electronics

Superconducting electronics (SCE) is uniquely suited to implement neuromorphic systems. As a result, SCE has the potential to enable a new generation of neuromorphic architectures that can simultaneously provide scalability, programmability, biological fidelity, on-line learning support, efficiency and speed. Supporting all of these capabilities simultaneously has thus far proven to be difficult using existing semiconductor technologies. However, as the fields of computational neuroscience and artificial intelligence (AI) continue to advance, the need for architectures that can provide combinations of these capabilities will grow. In this paper, we will explain how superconducting electronics could be used to address this need by combining analog and digital SCE circuits to build large scale neuromorphic systems. In particular, we will show through detailed analysis that the available SCE technology is suitable for near term neuromorphic demonstrations. Furthermore, this analysis will establish that neuromorphic architectures built using SCE will have the potential to be significantly faster and more efficient than current approaches, all while supporting capabilities such as biologically suggestive neuron models and on-line learning. In the future, SCE-based neuromorphic systems could serve as experimental platforms supporting investigations that are not feasible with current approaches. Ultimately, these systems and the experiments that they support would enable the advancement of neuroscience and the development of more sophisticated AI.

A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.

Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions

We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Signal Filtering Enabled by Spike Voltage-Dependent Plasticity in Metalloporphyrin-Based Memristors

Neural systems can selectively filter and memorize spatiotemporal information, thus enabling high-efficient information processing. Emulating such an exquisite biological process in electronic devices is of fundamental importance for developing neuromorphic architectures with efficient in situ edge/parallel computing, and probabilistic inference. Here a novel multifunctional memristor is proposed and demonstrated based on metalloporphyrin/oxide hybrid heterojunction, in which the metalloporphyrin layer allows for dual electronic/ionic transport. Benefiting from the coordination-assisted ionic diffusion, the device exhibits smooth, gradual conductive transitions. It is shown that the memristive characteristics of this hybrid system can be modulated by altering the metal center for desired metal-oxygen bonding energy and oxygen ions migration dynamics. The spike voltage-dependent plasticity stemming from the local/extended movement of oxygen ions under low/high voltage is identified, which permits potentiation and depression under unipolar different positive voltages. As a proof-of-concept demonstration, memristive arrays are further built to emulate the signal filtering function of the biological visual system. This work demonstrates the ionic intelligence feature of metalloporphyrin and paves the way for implementing efficient neural-signal analysis in neuromorphic hardware.

Dynamic Processes in a Superconducting Adiabatic Neuron with Non-Shunted Josephson Contacts

We investigated the dynamic processes in a superconducting neuron based on Josephson contacts without resistive shunting (SC-neuron). Such a cell is a key element of perceptron-type neural networks that operate in both classical and quantum modes. The analysis of the obtained results allowed us to find the mode when the transfer characteristic of the element implements the “sigmoid” activation function. The numerical approach to the analysis of the equations of motion and the Monte Carlo method revealed the influence of inertia (capacitances), dissipation, and temperature on the dynamic characteristics of the neuron.

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.

Low-temperature emergent neuromorphic networks with correlated oxide devices

Neuromorphic computing-which aims to mimic the collective and emergent behavior of the brain's neurons, synapses, axons, and dendrites-offers an intriguing, potentially disruptive solution to society's ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

Theoretical Basis of Quantum-Mechanical Modeling of Functional Nanostructures

The paper presents an analytical review of theoretical methods for modeling functional nanostructures. The main evolutionary changes in the approaches of quantum-mechanical modeling are described. The foundations of the first-principal theory are considered, including the stationery and time-dependent Schrödinger equations, wave functions, the form of writing energy operators, and the principles of solving equations. The idea and specifics of describing the motion and interaction of nuclei and electrons in the framework of the theory of the electron density functional are presented. Common approximations and approaches in the methods of quantum mechanics are presented, including the Born–Oppenheimer approximation, the Hartree–Fock approximation, the Thomas–Fermi theory, the Hohenberg–Kohn theorems, and the Kohn–Sham formalism. Various options for describing the exchange–correlation energy in the theory of the electron density functional are considered, such as the local density approximation, generalized and meta-generalized gradient approximations, and hybridization of the generalized gradient method. The development of methods of quantum mechanics to quantum molecular dynamics or the dynamics of Car–Parrinello is shown. The basic idea of combining classical molecular modeling with calculations of the electronic structure, which is reflected in the potentials of the embedded atom, is described.

Neuron-like spiking and bursting in Josephson junctions: A review

The superconducting Josephson junction shows spiking and bursting behaviors, which have similarities with neuronal spiking and bursting. This phenomenon had been observed long ago by some researchers; however, they overlooked the biological similarity of this particular dynamical feature and never attempted to interpret it from the perspective of neuronal dynamics. In recent times, the origin of such a strange property of the superconducting junction has been explained and such neuronal functional behavior has also been observed in superconducting nanowires. The history of this research is briefly reviewed here with illustrations from studies of two junction models and their dynamical interpretation in the sense of biological bursting.

Roadmap on emerging hardware and technology for machine learning

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Synthesis, Characterization, and Magnetoresistive Properties of the Epitaxial Pd0.96Fe0.04/VN/Pd0.92Fe0.08 Superconducting Spin-Valve Heterostructure

A thin-film superconductor(S)/ferromagnet(F) F1/S/F2-type Pd0.96Fe0.04(20 nm)/VN(30 nm)/Pd0.92Fe0.08(12 nm) heteroepitaxial structure was synthesized on (001)-oriented single-crystal MgO substrate utilizing a combination of the reactive magnetron sputtering and the molecular-beam epitaxy techniques in ultrahigh vacuum conditions. The reference VN film, Pd0.96Fe0.04/VN, and VN/Pd0.92Fe0.08 bilayers were grown in one run with the target sample. In-situ low-energy electron diffraction and ex-situ X-ray diffraction investigations approved that all the Pd1-xFex and VN layers in the series grew epitaxial in a cube-on-cube mode. Electric resistance measurements demonstrated sharp transitions to the superconducting state with the critical temperature reducing gradually from 7.7 to 5.4 K in the sequence of the VN film, Pd0.96Fe0.04/VN, VN/Pd0.92Fe0.08, and Pd0.96Fe0.04/VN/Pd0.92Fe0.08 heterostructures due to the superconductor/ferromagnet proximity effect. Transition width increased in the same sequence from 21 to 40 mK. Magnetoresistance studies of the trilayer Pd0.96Fe0.04/VN/Pd0.92Fe0.08 sample revealed a superconducting spin-valve effect upon switching between the parallel and antiparallel magnetic configurations, and anomalies associated with the magnetic moment reversals of the ferromagnetic Pd0.92Fe0.08 and Pd0.96Fe0.04 alloy layers. The moderate critical temperature suppression and manifestations of superconducting spin-valve properties make this kind of material promising for superconducting spintronics applications.

Magnetotransport Properties of Ferromagnetic Nanoparticles in a Semiconductor Matrix Studied by Precise Size-Selective Cluster Ion Beam Deposition

The combination of magnetic and semiconducting properties in one material system has great potential for integration of emerging spintronics with conventional semiconductor technology. One standard route for the synthesis of magnetic semiconductors is doping of semiconductors with magnetic atoms. In many semiconductor–magnetic–dopant systems, the magnetic atoms form precipitates within the semiconducting matrix. An alternative and controlled way to realize such nanocomposite materials is the assembly by co-deposition of size-selected cluster ions and a semiconductor. Here we follow the latter approach to demonstrate that this fabrication route can be used to independently study the influence of cluster concentration and cluster size on magneto-transport properties. In this case we study Fe clusters composed of approximately 500 or 1000 atoms soft-landed into a thermally evaporated amorphous Ge matrix. The analysis of field and temperature dependent transport shows that tunneling processes affected by Coulomb blockade dominate at low temperatures. The nanocomposites show saturating tunneling magnetoresistance, additionally superimposed by at least one other effect not saturating upon the maximum applied field of 6 T. The nanocomposites’ resistivity and the observed tunneling magnetoresistance depend exponentially on the average distance between cluster surfaces. On the contrary, there is no notable influence of the cluster size on the tunneling magnetoresistance.

Engineering Complex Synaptic Behaviors in a Single Device: Emulating Consolidation of Short-term Memory to Long-term Memory in Artificial Synapses via Dielectric Band Engineering

As one of the key neuronal activities associated with memory in the human brain, memory consolidation is the process of the transition of short-term memory (STM) to long-term memory (LTM), which transforms an external stimulus to permanently stored information. Here, we report the emulation of this complex synaptic function, consolidation of STM to LTM, in a single-crystal indium phosphide (InP) field effect transistor (FET)-based artificial synapse. This behavior is achieved via the dielectric band and charge trap lifetime engineering in a dielectric gate heterostructure of aluminum oxide and titanium oxide. We analyze the behavior of these complex synaptic functions by engineering a variety of action potential parameters, and the devices exhibit good endurance, long retention time (>10⁵ s), and high uniformity. Uniquely, this approach utilizes growth and device fabrication techniques which are scalable and back-end CMOS compatible, making this InP synaptic device a potential building block for neuromorphic computing.

Controlling the proximity effect in a Co/Nb multilayer: the properties of electronic transport

We present both theoretical and experimental investigations of the proximity effect in a stack-like superconductor/ferromagnetic (S/F) superlattice, where ferromagnetic layers with different thicknesses and coercive fields are made of Co. Calculations based on the Usadel equations allow us to find the conditions at which switching from the parallel to the antiparallel alignment of the neighboring F-layers leads to a significant change of the superconducting order parameter in superconductive thin films. We experimentally study the transport properties of a lithographically patterned Nb/Co multilayer. We observe that the resistive transition of the multilayer structure has multiple steps, which we attribute to the transition of individual superconductive layers with the critical temperature, T _c, depending on the local magnetization orientation of the neighboring F-layers. We argue that such superlattices can be used as tunable kinetic inductors designed for artificial neural networks representing the information in a "current domain".

Stimuli-Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives

Brain-inspired neuromorphic computing is intended to provide effective emulation of the functionality of the human brain via the integration of electronic components. Recent studies of synaptic plasticity, which represents one of the most significant neurochemical bases of learning and memory, have enhanced the general comprehension of how the brain functions and have thereby eased the development of artificial neuromorphic devices. An understanding of the synaptic plasticity induced by various types of stimuli is essential for neuromorphic system construction. The realization of multiple stimuli-enabled synapses will be important for future neuromorphic computing applications. In this Review, state-of-the-art synaptic devices with particular emphasis on their synaptic behaviors under excitation by a variety of external stimuli are summarized, including electric fields, light, magnetic fields, pressure, and temperature. The switching mechanisms of these synaptic devices are discussed in detail, including ion migration, electron/hole transfer, phase transition, redox-based resistive switching, and other mechanisms. This Review aims to provide a comprehensive understanding of the operating mechanisms of artificial synapses and thus provides the principles required for design of multifunctional neuromorphic systems with parallel processing capabilities.

Three-Dimensional Nanoscale Flexible Memristor Networks with Ultralow Power for Information Transmission and Processing Application

To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

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.

Optoelectronic Synapses Based on Hot-Electron-Induced Chemical Processes

Highly efficient information processing in the brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here, we have developed an artificial synapse with both electrical and optical memory effects using chemical transformations in plasmonic tunnel junctions. In an electronic implementation, the electrons tunneled into plasmonic nanorods under a low bias voltage are harvested to write information into the tunnel junctions via hot-electron-mediated chemical reactions with the environment. In an optical realization, the information can be written by an external light illumination to excite hot electrons in the plasmonic nanorods. The stored information is nonvolatile and can be read either electrically or optically by measuring the resistance or inelastic-tunneling-induced light emission, respectively. The described architecture provides a high density (∼10¹⁰ cm^-2) of memristive optoelectronic devices which can be used as multilevel nonvolatile memory, logic units, or artificial synapses in future electronic, optoelectronic, and artificial neural networks.

Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.

Synaptic weighting in single flux quantum neuromorphic computing

Josephson junctions act as a natural spiking neuron-like device for neuromorphic computing. By leveraging the advances recently demonstrated in digital single flux quantum (SFQ) circuits and using recently demonstrated magnetic Josephson junction (MJJ) synaptic circuits, there is potential to make rapid progress in SFQ-based neuromorphic computing. Here we demonstrate the basic functionality of a synaptic circuit design that takes advantage of the adjustable critical current demonstrated in MJJs and implement a synaptic weighting element. The devices were fabricated with a restively shunted Nb/AlO_x-Al/Nb process that did not include MJJs. Instead, the MJJ functionality was tested by making multiple circuits and varying the critical current, but not the external shunt resistance, of the oxide Josephson junction that represents the MJJ. Experimental measurements and simulations of the fabricated circuits are in good agreement.

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications.

Design of a Power Efficient Artificial Neuron Using Superconducting Nanowires

With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In particular, spiking neural networks (SNNs) offer a bio-realistic approach, relying on pulses, analogous to action potentials, as units of information. While software encoded networks provide flexibility and precision, they are often computationally expensive. As a result, hardware SNNs based on the spiking dynamics of a device or circuit represent an increasingly appealing direction. Here, we propose to use superconducting nanowires as a platform for the development of an artificial neuron. Building on an architecture first proposed for Josephson junctions, we rely on the intrinsic non-linearity of two coupled nanowires to generate spiking behavior, and use electrothermal circuit simulations to demonstrate that the nanowire neuron reproduces multiple characteristics of biological neurons. Furthermore, by harnessing the non-linearity of the superconducting nanowire's inductance, we develop a design for a variable inductive synapse capable of both excitatory and inhibitory control. We demonstrate that this synapse design supports direct fan-out, a feature that has been difficult to achieve in other superconducting architectures, and that the nanowire neuron's nominal energy performance is competitive with that of current technologies.

Richardson diffusion in neurons

The dynamics of an initial wave packet affected by random noise is considered in the framework of a comb model. The model is relevant to a diffusion problem in neurons where the transport of ions can be accelerated by an external random field due to synapse fluctuations. In the present specific case, it acts as boundary conditions, which lead to a reaction transport equation with multiplicative noise. The temporal behavior of the mean squared displacement is estimated analytically, and it is shown that the spreading of the initial wave packet corresponds to Richardson diffusion.

Characterization of Uniformity in Nb/NbxSi1-x/Nb Josephson Junctions

The uniformity of the barriers in Josephson junctions (JJs) is a critical parameter in determining performance and operating margins for a wide variety of superconducting electronic circuits. We present an automated measurement system capable of measuring individual JJs across a 1 × 1 cm die at both ambient temperature and 4 K. This technique allows visualization of the spatial variation over a large area of the critical electrical properties of the junctions and allows for the direct correlation between room-temperature (RT) resistance and low temperature properties. The critical current variation of NbxSi1-x (x = 15%) barriers is found to be about 2.6% (one standard deviation) for 1024 junctions across an individual die and only weakly correlates with RT resistance measurements.