Neuromorphic Functions Research Articles

Enzyme-Mediated Organic Neurohybrid Synapses.

The development of organic artificial synapses that exhibit biomimicry features also may enable a more seamless integration of neuroelectronic devices in the nervous system, allowing artificial neuromodulation to be perceived as natural behavior by neuronal cells. Nevertheless, the capability to interact with both electroactive and non-electroactive neurotransmitters remains a challenge since state-of-the-art devices mainly rely on the oxidation of electroactive species. Here, the study proposes an organic artificial synapse engineered to enable interaction with non-electroactive species present in the central nervous system. By integrating a conductive polymeric film functionalized with platinum nanoparticles, the device can catalyze the oxidation of electroactive molecules (i.e., H2O2) resulting from neurotransmitter-specific enzymatic reactions following an enzymatic functionalization, therefore exhibiting neuromorphic functions driven by non-electroactive neurotransmitters. The creation of devices that can interact with or monitor these neurotransmitters can be seen as a significant step toward innovative technologies to expand the understanding of the mechanisms underlying neurological disorders and the development of novel, more effective treatments for them.

Bioactive Ion-Confined Ultracapacitive Memristors with Neuromorphic Functions.

The field of bioinspired iontronics, bridging electronic devices and ionic systems, has multiple biological applications. Carbon-based ultracapacitive devices hold promise for controlling bioactive ions via electric double layers due to their high-surface-area and biocompatible porous carbon electrodes. However, the interplay between complex bioactive ions and porous carbons remains unclear due to the variety of structures of bioactive ions present in biological systems. Herein, we investigate the adsorption behavior of a series of bioactive ammonium-based cations with varying alkyl chain lengths in nanoporous carbons. We find that strong physisorption results from the synergistic hydrophobic interaction and electrostatic attraction between porous carbons (with a negative zeta potential) and bioactive cations. Bioactive cations with varying alkyl chain lengths can be irreversibly physically adsorbed and confined within nanoporous carbons resulting in anion enrichment and depletion during electric polarization. This situation, in turn, results in a characteristic memristive behavior in all-carbon capacitive ionic memristor devices. Our findings highlight the relationship between the resistance state of the memristor and ion adsorption mechanisms in all-carbon capacitive devices, which hold potential for future transmitter delivery, biointerfacing, and neuromorphic devices.

Bioactive Ion‐Confined Ultracapacitive Memristors with Neuromorphic Functions

The field of bioinspired iontronics, bridging electronic devices and ionic systems, has multiple biological applications. Carbon‐based ultracapacitive devices hold promise for controlling bioactive ions via electric double layers due to their high‐surface‐area and biocompatible porous carbon electrodes. However, the interplay between complex bioactive ions and porous carbons remains unclear due to the variety of structures of bioactive ions present in biological systems. Herein, we investigate the adsorption behavior of a series of bioactive ammonium‐based cations with varying alkyl chain lengths in nanoporous carbons. We find that strong physisorption results from the synergistic hydrophobic interaction and electrostatic attraction between porous carbons (with a negative zeta potential) and bioactive cations. Bioactive cations with varying alkyl chain lengths can be irreversibly physically adsorbed and confined within nanoporous carbons resulting in anion enrichment and depletion during electric polarization. This situation, in turn, results in a characteristic memristive behavior in all‐carbon capacitive ionic memristor devices. Our findings highlight the relationship between the resistance state of the memristor and ion adsorption mechanisms in all‐carbon capacitive devices, which hold potential for future transmitter delivery, biointerfacing, and neuromorphic devices.

Heterogating Gel Iontronics: A Revolution in Biointerfaces and Ion Signal Transmission.

Currently, existing iontronic systems are limited and struggle to process electronic-to-multi-ionic transport, resulting in interchange inefficiencies and incompatibilities between artificial ion devices and biological tissue interfaces. The development of heterogating gel iontronics offers a significant advancement in bridging this gap, drawing inspiration from the complex ionic transmission mechanisms found in biological synapses within neural networks. These heterogating gels utilize a biphasic architecture, where the heterointerface effect constructs ionic transfer energy barriers, enabling distinct signal transmission among different ions. In systems with multiple ion species, heterogating gel iontronics allow for precise control of ion transmission, realizing hierarchical and selective cross-stage signal transmission as a neuromorphic function. This perspective highlights the vast potential of heterogating iontronics in applications such as biosensing, neuroprosthetics, and ion separation technologies. Meanwhile, it also addresses the current challenges, including scaling production, ensuring biocompatibility, and integrating with existing technologies, which are crucial for future development. The advancement of heterogating gels is expected to promote the integration between abiotic and biotic systems, with broad implications for smart sensors, bioneural devices, and beyond.

Emerging Optoelectronic Devices for Brain‐Inspired Computing

AbstractBrain‐inspired neuromorphic computing is recognized as a promising technology for implementing human intelligence in hardware. Neuromorphic devices, including artificial synapses and neurons, are regarded as essential components for the construction of neuromorphic hardware systems. Recently, optoelectronic neuromorphic devices are increasingly highlighted due to their potential applications in next‐generation artificial visual systems, attributed to their integrated sensing, computing, and memory capabilities. In this review, recent advancements in optoelectronic synapses and neurons are examined, with an emphasis on their structural characteristics, operational principles, and the replication of neuromorphic functions. For optoelectronic synaptic devices, such as memristor‐ and transistor‐based ones, attention is given to the two primary weight update modes: the light‐electricity synergistic mode and the all‐optical mode. Optoelectronic neurons are discussed in terms of different device types, including threshold switch neurons and semiconductor laser neurons. Last, the challenges that impede the progress of optoelectronic neuromorphic devices are identified, and potential future directions are suggested.

Optoelectronic synapses with chemical-electric behaviors in gallium nitride semiconductors for biorealistic neuromorphic functionality

Optoelectronic synapses, leveraging the integration of classic photo-electric effect with synaptic plasticity, are emerging as building blocks for artificial vision and photonic neuromorphic computing. However, the fundamental working principles of most optoelectronic synapses mainly rely on physical behaviors while missing chemical-electric synaptic processes critical for mimicking biorealistic neuromorphic functionality. Herein, we report a photoelectrochemical synaptic device based on p-AlGaN/n-GaN semiconductor nanowires to incorporate chemical-electric synaptic behaviors into optoelectronic synapses, demonstrating unparalleled dual-modal plasticity and chemically-regulated neuromorphic functions through the interplay of internal photo-electric and external electrolyte-mediated chemical-electric processes. Electrical modulation by implementing closed or open-circuit enables switching of optoelectronic synaptic operation between short-term and long-term plasticity. Furthermore, inspired by transmembrane receptors that connect extracellular and intracellular events, synaptic responses can also be effectively amplified by applying chemical modifications to nanowire surfaces, which tune external and internal charge behaviors. Notably, under varied external electrolyte environments (ion/molecule species and concentrations), our device successfully mimics chemically-regulated synaptic activities and emulates intricate oxidative stress-induced biological phenomena. Essentially, we demonstrate that through the nanowire photoelectrochemical synapse configuration, optoelectronic synapses can be incorporated with chemical-electric behaviors to bridge the gap between classic optoelectronic synapses and biological synapses, providing a promising platform for multifunctional neuromorphic applications.

Nanocomposites of ferrocenyl-modified gold clusters and semiconducting polymers that integrate field-effect transistor and flash memory in a single neuromorphic device

We demonstrate that electroactive thin films incorporating semiconducting polymers and deterministic functionalized gold nanoclusters (ncAu25) lead to the integration of the functions of resistive memory device and field-effect transistor within a single component (“mem-transistor”) in a neuromorphic system. Memristor functions originate from ferrocenyl-modified gold nanoclusters (ncAu25-Fc) embedded in polymethyl-methacrylate (PMMA) and devices optimized for maximum 1/0 “flash” memory effect are found to contain 15 wt. % ncAu25-Fc. Integrated memristor and neuromorphic functions are obtained by replacing PMMA with poly(3-hexylthiophene) (P3HT) in the active layer, from which transistor effects are derived. Based on the energy band diagrams of ncAu25, PMMA, and P3HT, percolation theory is used to explain the memristor 1/0 on/off ratio as a function of ncAu25-Fc concentration. The use of ncAu25-Fc with charge-tunable, ferrocene-modified ligands is critical to achieve better cluster–polymer interfaces. Our work shows that nanostructures of polymers and metalorganic frameworks bear strong potential in the field of neuromorphic devices and circuital simplification of data storage technology.

(Invited) Towards an Expanded Palette of Materials and Mechanisms for Neuromorphic Computing

The metal-insulator transitions of electron correlated transition metal oxides provide an attractive vector for achieving large conductance switching with minimal energy dissipation. However, given the sparse and disconnected current knowledge of neuromorphic materials, a fundamental understanding of descriptors of neuromorphic function formulated in terms of intrinsic material properties, and the influence of atomistic defects on mesoscale domain evolution in the presence of external applied fields is currently lacking. Using VO2 and M x V2O5 compounds as model systems, I will detail our efforts to develop a systematic understanding of how compositional modifications through substitutional or interstitial doping alter transformation characteristics such as the transition temperature, magnitude of switching, energy dissipation, and hysteresis.The inclusion of dopant atoms strongly modifies the free energy landscapes in terms of relative phase stabilities, transformation barriers, and pathways; thereby profoundly altering the coupling of lattice, electronic, and spin degrees of freedom in a non-trivial manner. I will particularly focus on the three distinct mechanisms: (a) discovery of diffusive dopants that provide a distinctive new way to alter the dynamics of electronic transitions; (b) cation shuttling and polaron oscillation as a means of engendering metal—insulator transitions; and (c) lattice-anharmonicity-driven mechanisms in compounds with stereochemically active lone pairs. Acknowledgements: Research supported as part of REMIND, an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under DE-SC0023353

Investigation of Neuromorphic Behaviors in Access Regions-Integrated Van Der Waals Heterostructures

The emergence of neuromorphic devices has disrupted the traditional von Neumann architecture, providing a solution to the challenges of simulating neural parallel processing and power consumption in conventional computer architectures [1]. Access regions (ARs) are strategically created and positioned in proximity to source and drain electrodes, resulting in a channel that does not overlap with the floating gate (FG) [2]. In this study, we investigated ARs integrated van der Waals heterostructural field-effect transistor (AR-VH-FET) device, constructed by vertically stacking several 2D materials. Specifically, molybdenum disulfide (MoS2) served as a channel, hexagonal boron nitride (h-BN) as a tunneling layer, and graphene (Gr) as the FG. We conducted measurements on the transfer and output characteristics to characterize the electrical performance of the AR-VH-FET. Under a fixed VDS, with the application of positive and negative VBG as programming and erasing voltages respectively, we measured device retention and endurance. AR-VH-FET devices not only emulate basic neuromorphic functions but also replicate biological characteristics.

(Invited) Enabling Bio-Realistic Artificial Intelligence Hardware with Neuromorphic Nanoelectronics

The exponentially improving performance of digital computers has recently slowed due to the speed and power consumption issues resulting from the von Neumann bottleneck. In contrast, neuromorphic computing aims to circumvent these limitations by spatially co-locating logic and memory in a manner analogous to biological neuronal networks [1]. Beyond reducing power consumption, neuromorphic devices provide efficient architectures for image recognition, machine learning, and artificial intelligence [2]. This talk will explore how low-dimensional nanoelectronic materials enable gate-tunable neuromorphic devices [3]. For example, by utilizing self-aligned, atomically thin heterojunctions, dual-gated Gaussian transistors have been realized, which show tunable anti-ambipolarity for artificial neurons, competitive learning, spiking circuits, and mixed-kernel support vector machines [4,5]. In addition, field-driven defect motion in polycrystalline monolayer MoS2 enables gate-tunable memristive phenomena that serve as the basis of hybrid memristor/transistor devices (i.e., ‘memtransistors’) that concurrently provide logic and data storage functions [6]. The planar geometry of memtransistors further allows multiple contacts and dual gating that mimic the behavior of biological systems such as heterosynaptic responses [7]. Moreover, control over polycrystalline grain structure enhances the tunability of potentiation and depression, which enables unsupervised continuous learning in spiking neural networks [8]. Finally, the moiré potential in asymmetric twisted bilayer graphene/hexagonal boron nitride heterostructures gives rise to robust electronic ratchet states. The resulting hysteretic, non-volatile injection of charge carriers enables room-temperature operation of moiré synaptic transistors with diverse bio-realistic neuromorphic functionalities and efficient compute-in-memory designs for low-power artificial intelligence and machine learning hardware [9].[1] V. K. Sangwan, et al., Matter, 5, 4133 (2022).[2] V. K. Sangwan, et al., Nature Nanotechnology, 15, 517 (2020).[3] M. E. Beck, et al., ACS Nano, 14, 6498 (2020).[4] M. E. Beck, et al., Nature Communications, 11, 1565 (2020).[5] X. Yan, et al., Nature Electronics, DOI: 10.1038/s41928-023-01042-7 (2023).[6] X. Yan, et al., Advanced Materials, 34, 2108025 (2022).[7] H.-S. Lee, et al., Advanced Functional Materials, 30, 2003683 (2020).[8] J. Yuan, et al., Nano Letters, 21, 6432 (2021).[9] X. Yan, et al., Nature, DOI: 10.1038/s41586-023-06791-1 (2023).

Liquid‐Solid Combination Memristors with Switchable Resistance

AbstractHere a reconfigurable memristor is demonstrated by connecting ZnO film to a fluidic channel. The memristive characteristics are successfully demonstrated with an electrolyte solution. The benefit of using a microfluidic channel is that the memristive characteristics can be adjusted by changing the electrolyte solution in real‐time. The neuromorphic functions such as long‐term plasticity, Spiking‐Rate‐Dependent Plasticity (SRDP), and behavior associated with “learning experiences”are demostrated in the devices. The capability of real‐time manipulating of memristive characteristics enables diverse manipulations on memristive characteristics of the devices, by doping of different concentrated and type of ions in the ZnO film. The electrolyte solution will open new possibilities for resistance switch manipulations, for the next generation neuromorphic computing.

Memristive Characteristics in an Asymmetrically Charged Nanochannel.

The emergent nanofluidic memristor provides a promising way of emulating neuromorphic functions in the brain. The conical-shaped nanopore showed promising features for a nanofluidic memristor, inspiring us to investigate the memory effects in asymmetrically charged nanochannels due to their high current rectification, which may result in good memory effects. Here, the memory effects of an asymmetrically charged nanofluidic channel were numerically simulated by Poisson-Nernst-Planck equations. Our results showed that the I-V curves represented a diode in low scanning frequency and then became a memristor and finally a resistor as frequency increased. We successfully replicated the learning behavior in our system with history-dependent ion redistribution in the nanochannel. Some critical factors were quantitatively analyzed for the memory effects including voltage amplitude, optimal frequency, and Dukhin number. Experimental characterizations were also carried out. Our findings are useful for the design of nanofluidic memristors by the principle of enrichment and depletion as well as the determination of the best memory settings.

Exploring non-steady-state charge transport dynamics in information processing: insights from reservoir computing

Exploring nonlinear chemical dynamic systems for information processing has emerged as a frontier in chemical and computational research, seeking to replicate the brain’s neuromorphic and dynamic functionalities. In this study, we have extensively explored the information processing capabilities of a nonlinear chemical dynamic system through theoretical simulation by integrating a non-steady-state proton-coupled charge transport system into reservoir computing (RC) architecture. Our system demonstrated remarkable success in tasks such as waveform recognition, voice identification and chaos system prediction. More importantly, through a quantitative study, we revealed that the alignment between the signal processing frequency of the RC and the characteristic time of the dynamics of the nonlinear system plays a crucial role in this physical reservoir’s performance, directly influencing the efficiency in the task execution, the reservoir states and the memory capacity. The processing frequency range was further modulated by the characteristic time of the dynamic system, resulting in an implementation akin to a ‘chemically-tuned band-pass filter’ for selective frequency processing. Our study thus elucidates the fundamental requirements and dynamic underpinnings of the non-steady-state charge transport dynamic system for RC, laying a foundational groundwork for the application of dynamical molecular scale devices for in-materia neuromorphic computing.

Organic Synaptic Transistors with Environmentally Friendly Core/Shell Quantum Dots for Wavelength-Selective Memory and Neuromorphic Functions.

Organic transistors based on organic semiconductors together with quantum dots (QDs) are attracting more and more interest because both materials have excellent optoelectronic properties and solution processability. Electronics based on nontoxic QDs are highly desired considering the potential health risks but are limited by elevated surface defects, inadequate stability, and diminished luminescent efficiency. Herein, organic synaptic transistors based on environmentally friendly ZnSe/ZnS core/shell QDs with passivating surface defects are developed, exhibiting optically programmable and electrically erasable characteristics. The synaptic transistors feature linear multibit storage capability and wavelength-selective memory function with a retention time above 6000 s. Various neuromorphic applications, including memory enhancement, optical communication, and memory consolidation behaviors, are simulated. Utilizing an established neuromorphic model, accuracies of 92% and 91% are achieved in pattern recognition and complicated electrocardiogram signal processing, respectively. This research highlights the potential of environmentally friendly QDs in neuromorphic applications and health monitoring.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device’s superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

Versatile Cu2ZnSnS4-based synaptic memristor for multi-field-regulated neuromorphic applications.

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single synaptic memristive device holds the most potential for realizing next-generation neuromorphic systems, but is still challenging yet achievable. Herein, a simple bi-terminal optoelectronic synaptic memristor is newly proposed based on kesterite Cu2ZnSnS4, exhibiting stable nonvolatile resistive switching with excellent spatial uniformity and unique optoelectronic synaptic behaviors. The device demonstrates not only low switching voltage (-0.39 ± 0.08V), concentrated Set/Reset voltage distribution (<0.08/0.15V), and long retention time (>104s) but also continuously modulable conductance by both electric (different width/interval/amplitude) and light (470-808nm with different intensity) stimulus. These advantages make the device good electrically and optically simulated synaptic functions, including excitatory and inhibitory, paired-pulsed facilitation, short-/long-term plasticity, spike-timing-dependent plasticity, and "memory-forgetting" behavior. Significantly, decimal arithmetic calculation (addition, subtraction, and commutative law) is realized based on the linear conductance regulation, and high precision pattern recognition (>88%) is well achieved with an artificial neural network constructed by 5 × 5 × 4 memristor array. Predictably, such kesterite-based optoelectronic memristors can greatly open the possibility of realizing multi-functional neuromorphic systems.

Synaptic Plasticity and Visual Memory in a Neuromorphic 2D Memitter Based on WS2 Monolayers

AbstractNeuromorphic computing aims to leverage physical phenomena of adaptive materials for emulating information processing capabilities and effectiveness of biological neuronal circuits. In this framework, memristors (resistors with memory) based on 2D materials are demonstrated for the hardware implementation of highly integrated artificial neural networks. All the works reported thus far exploited electrical properties of 2D materials to emulate neuromorphic functionalities. Here, a 2D memitter (emitter with memory) is reported on that exploits the stimuli‐responsive photoluminescence of a monolayer WS2 for neuromorphic‐type of data processing. A combined experimental and modeling approach reveals that photoluminescent dynamics triggered by optical stimulation emulates Short‐Term synaptic Plasticity and Visual Short‐Term Memory typical of biological systems. While spatio‐temporal processing capabilities of input signals can be used for information processing in the context of reservoir computing, the capability of the 2D memitter of sensing, processing, and memorizing‐forgetting optical inputs in the same physical substrate can be utilized for in‐sensor computing.

All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing.

Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.

Nonlinear erasing of propagating spin-wave pulses in thin-film Ga:YIG

Nonlinear phenomena are crucial for magnon-based information processing, but the nonlinear interaction between two spin-wave signals requires their spatiotemporal overlap, which can be challenging for directional processing devices. Our study focuses on a gallium-substituted yttrium iron garnet film, which exhibits an exchange-dominated dispersion relation and, thus, provides a particularly broad range of group velocities compared to pure YIG. Using time- and space-resolved Brillouin light scattering spectroscopy, we demonstrate the excitation of time-separated spin-wave pulses at different frequencies from the same source, where the delayed pulse catches up with the previously excited pulse and outruns it due to its higher group velocity. By varying the excitation power of the faster pulse, the outcome can be finely tuned from a linear superposition to a nonlinear interaction of both pulses, resulting in a full attenuation of the slower pulse. Therefore, our findings demonstrate the all-magnonic erasing process of a propagating magnonic signal, which enables the realization of complex temporal logic operations with potential application, e.g., in inhibitory neuromorphic functionalities.

Gate-Controlled Neuromorphic Functional Transition in an Electrochemical Graphene Transistor.

Neuromorphic devices have attracted significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural networks such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between carbon atoms and hydrogen ions, we can effectively manipulate the electric conductivity of graphene transistors, resulting in a high on/off resistance ratio, a well-defined set/reset voltage, and a prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity for developing adaptive neural networks for the upcoming era of artificial intelligence and machine learning.