Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10³ μm² devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

Multilevel Ultrafast Flexible Nanoscale Nonvolatile Hybrid Graphene Oxide-Titanium Oxide Memories

Graphene oxide (GO) resistive memories offer the promise of low-cost environmentally sustainable fabrication, high mechanical flexibility and high optical transparency, making them ideally suited to future flexible and transparent electronics applications. However, the dimensional and temporal scalability of GO memories, i.e., how small they can be made and how fast they can be switched, is an area that has received scant attention. Moreover, a plethora of GO resistive switching characteristics and mechanisms has been reported in the literature, sometimes leading to a confusing and conflicting picture. Consequently, the potential for graphene oxide to deliver high-performance memories operating on nanometer length and nanosecond time scales is currently unknown. Here we address such shortcomings, presenting not only the smallest (50 nm), fastest (sub-5 ns), thinnest (8 nm) GO-based memory devices produced to date, but also demonstrate that our approach provides easily accessible multilevel (4-level, 2-bit per cell) storage capabilities along with excellent endurance and retention performance-all on both rigid and flexible substrates. Via comprehensive experimental characterizations backed-up by detailed atomistic simulations, we also show that the resistive switching mechanism in our Pt/GO/Ti/Pt devices is driven by redox reactions in the interfacial region between the top (Ti) electrode and the GO layer.

Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol

Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.

Coupling Resistive Switching Devices with Neurons: State of the Art and Perspectives

Here we provide the state-of-the-art of bioelectronic interfacing between biological neuronal systems and artificial components, focusing the attention on the potentiality offered by intrinsically neuromorphic synthetic devices based on Resistive Switching (RS). Neuromorphic engineering is outside the scopes of this Perspective. Instead, our focus is on those materials and devices featuring genuine physical effects that could be sought as non-linearity, plasticity, excitation, and extinction which could be directly and more naturally coupled with living biological systems. In view of important applications, such as prosthetics and future life augmentation, a cybernetic parallelism is traced, between biological and artificial systems. We will discuss how such intrinsic features could reduce the complexity of conditioning networks for a more natural direct connection between biological and synthetic worlds. Putting together living systems with RS devices could represent a feasible though innovative perspective for the future of bionics.

Electronic and optical properties of oxygen vacancies in amorphous Ta2O5 from first principles

Oxygen vacancies are important defects considered to play a central role in the electronic and optical properties of tantalum pentoxide (Ta₂O₅) films and devices. Despite extensive experimental studies on oxygen vacancies in Ta₂O₅, the reported defect states are ambiguously identified due to the absence of accurate and conclusive theoretical evidence. Here we investigate the thermodynamic, electronic, and optical properties of oxygen vacancies in amorphous Ta₂O₅ by first-principles calculations based on hybrid-functional density functional theory (DFT). The calculated thermodynamic and optical transition levels are in good agreement with a broad range of diverse measured properties with various experimental methods, providing conclusive evidence for the identification of the defect states observed in experiments as originating from oxygen vacancies. Our calculations also predict the formation of spin-polarized polarons. Our results elucidate the fundamental atomistic properties of oxygen vacancies in various oxidation states as a function of growth conditions and provide guidance to control the properties of Ta₂O₅ films/devices.

Resistive switching of Pt/TiO x /Pt devices fabricated on flexible Parylene-C substrates

Pt/TiO _x /Pt resistive switching (RS) devices are considered to be amongst the most promising candidates in memristor family and the technology transfer to flexible substrates could open the way to new opportunities for flexible memory implementations. Hence, an important goal is to achieve a fully flexible RS memory technology. Nonetheless, several fabrication challenges are present and must be solved prior to achieving reliable device fabrication and good electronic performances. Here, we propose a fabrication method for the successful transfer of Pt/TiO _x /Pt stack onto flexible Parylene-C substrates. The devices were electrically characterised, exhibiting both digital and analogue memory characteristics, which are obtained by proper adjustment of pulsing schemes during tests. This approach could open new application possibilities of these devices in neuromorphic computing, data processing, implantable sensors and bio-compatible neural interfaces.

Emulation of synaptic metaplasticity in memristors

Recent studies have shown that nanoionic-based memristors can offer rich internal dynamics during ion movement that enables these solid-state devices to emulate various synaptic functions in biological systems naturally. The experimental observations can be explained within the 2nd-order memristor theoretical framework, which states that the device conductance (weight) can be determined by multiple internal state variables that can be modulated at different time scales and lead to different activity-dependent synaptic behaviors. Here, we show experimentally that not only the synaptic weight, but also synaptic plasticity (i.e. polarity and the rate of weight change) depends on the history of the input activities. This "plasticity of plasticity" resembles metaplasticity effects observed in biological systems, which have been found to facilitate neuron competition and stability. Specifically, we show that the memristor device may exhibit the same apparent weight (conductance) after experiencing different history of activities, but when subjected to additional, identical stimulation conditions, the device will however exhibit very different responses including the polarity and rate of weight (conductance) change. These findings serve to further our knowledge of fundamental physical mechanisms in memristors, and help advance adaptive artificial neuromorphic systems based on these emerging devices.

Tuning analog resistive switching and plasticity in bilayer transition metal oxide based memristive synapses

The existence of rich suboxide phases is favorable for increasing the number of weight states in transition metal oxide synapses.

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

The accumulation and extrusion of Ca²⁺ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca²⁺ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca²⁺, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.

Pulse Shape and Timing Dependence on the Spike-Timing Dependent Plasticity Response of Ion-Conducting Memristors as Synapses

Ion-conducting memristors comprised of the layered materials Ge2Se3/SnSe/Ag are promising candidates for neuromorphic computing applications. Here, the spike-timing dependent plasticity (STDP) application is demonstrated for the first time with a single memristor type operating as a synapse over a timescale of 10 orders of magnitude, from nanoseconds through seconds. This large dynamic range allows the memristors to be useful in applications that require slow biological times, as well as fast times such as needed in neuromorphic computing, thus allowing multiple functions in one design for one memristor type-a "one size fits all" approach. This work also investigated the effects of varying the spike pulse shapes on the STDP response of the memristors. These results showed that small changes in the pre- and postsynaptic pulse shape can have a significant impact on the STDP. These results may provide circuit designers with insights into how pulse shape affects the actual memristor STDP response and aid them in the design of neuromorphic circuits and systems that can take advantage of certain features in the memristor STDP response that are programmable via the pre- and postsynaptic pulse shapes. In addition, the energy requirement per memristor is approximated based on the pulse shape and timing responses. The energy requirement estimated per memristor operating on slower biological timescales (milliseconds to seconds) is larger (nanojoules range), as expected, than the faster (nanoseconds) operating times (~0.1 pJ in some cases). Lastly, the memristors responded in a similar manner under normal STDP conditions (pre- and post-spikes applied to opposite memristor terminals) as they did to the case where a waveform corresponding to the difference between pre- and post-spikes was applied to only one electrode, with the other electrode held at ground potential. By applying the difference signal to only one terminal, testing of the memristor in various applications can be achieved with a simplified test set-up, and thus be easier to accomplish in most laboratories.

Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing

Bio-inspired computing represents today a major challenge at different levels ranging from material science for the design of innovative devices and circuits to computer science for the understanding of the key features required for processing of natural data. In this paper, we propose a detail analysis of resistive switching dynamics in electrochemical metallization cells for synaptic plasticity implementation. We show how filament stability associated to joule effect during switching can be used to emulate key synaptic features such as short term to long term plasticity transition and spike timing dependent plasticity. Furthermore, an interplay between these different synaptic features is demonstrated for object motion detection in a spike-based neuromorphic circuit. System level simulation presents robust learning and promising synaptic operation paving the way to complex bio-inspired computing systems composed of innovative memory devices.

Microstructure and dynamics of vacancy-induced nanofilamentary switching network in donor doped SrTiO3-x memristors

Donor doping of perovskite oxides has emerged as an attractive technique to create high performance and low energy non-volatile analog memories. Here, we examine the origins of improved switching performance and stable multi-state resistive switching in Nb-doped oxygen-deficient amorphous SrTiO₃ (Nb:a-STO _x ) metal-insulator-metal (MIM) devices. We probe the impact of substitutional dopants (i.e., Nb) in modulating the electronic structure and subsequent switching performance. Temperature stability and bias/time dependence of the switching behavior are used to ascertain the role of substitutional dopants and highlight their utility to modulate volatile and non-volatile behavior in a-STO _x devices for adaptive and neuromorphic applications. We utilized a combination of transmission electron microscopy, photoluminescence emission properties, interfacial compositional evaluation, and activation energy measurements to investigate the microstructure of the nanofilamentary network responsible for switching. These results provide important insights into understanding mechanisms that govern the performance of donor-doped perovskite oxide-based memristive devices.

Nonassociative learning implementation by a single memristor-based multi-terminal synaptic device

Animals' survival is dependent on their abilities to adapt to the changing environment by adjusting their behaviours, which is related to the ubiquitous learning behaviour, nonassociative learning. Thus mimicking the indispensable learning behaviour in organisms based on electronic devices is vital to better achieve artificial neural networks and neuromorphic computing. Here a three terminal device consisting of an oxide-based memristor and a NMOS transistor is proposed. The memristor with gradual conductance tuning inherently functions as the synapse between sensor neurons and motor neurons and presents adjustable synaptic plasticity, while the NMOS transistor attached to the memristor is utilized to mimic the modulatory effect of the neuromodulator released by inter neurons. Such a memristor-based multi-terminal device allows the practical implementation of significant nonassociative learning based on a single electronic device. In this study, the experience-induced modification behaviour, both habituation and sensitization, was successfully achieved. The dependence of the nonassociative behavioural response on the strength and interval of presented stimuli was also discussed. The implementation of nonassociative learning offers feasible and experimental advantages for further study on neuromorphic systems based on electronic devices.

Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.

Enhanced non-volatile resistive switching in suspended single-crystalline ZnO nanowire with controllable multiple states

Resistive switching nanostructures are a promising candidate for next-generation non-volatile memories. In this report, we investigate the switching behaviors of single-crystalline ZnO nanowires suspended in air. They exhibit significantly higher current density, lower switching voltage, and more pronounced multiple conductance states compared to nanowires in direct contact with substrate. We attribute the effect to enhanced Joule heating efficiency, reduced surface scattering, and more significantly, the positive feedback established between the current density and local temperature in the suspended nanowires. The proposed mechanism has been quantitatively examined by finite element simulations. We have also demonstrated an innovative approach to initiating the current-temperature mutual enhancement through illumination by ultraviolet light, which further confirmed our hypothesis and enabled even greater enhancement. Our work provides further insight into the resistive switching mechanism of single-crystalline one-dimensional nanostructures, and suggests an effective means of performance enhancement and device optimization.

Electrode-induced digital-to-analog resistive switching in TaO x -based RRAM devices

In RRAM devices, electrodes play a significant role during the switching process. In this paper, different top electrodes are used for TaO y /Ta2O5-x /AlO σ triple-oxide-layer devices. Top electrode-induced digital resistive switching to analog resistive switching was observed. For Pt top electrode (TE) devices, abrupt digital resistive switching behavior was observed, while Al TE devices showed gradual analog resistive switching behavior. Devices with various AlO σ thicknesses and sizes were fabricated and characterized to evaluate the reliability of the analog resistive switching. The physical mechanisms responsible for this electrode-induced resistive switching behavior were discussed.

Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications

The first black-phosphorus synaptic device is demonstrated, which offers intrinsic anisotropy in its synaptic characteristics directly resulting from its low crystalline symmetry. Key features of biological synapses, such as long-term plasticity with heterogeneity, including long-term potentiation/depression and spike-timing-dependent plasticity, are mimicked. This demonstration represents an important step toward introducing intrinsic heterogeneity to artificial neuromorphic systems.

Compact Two-State-Variable Second-Order Memristor Model

A key requirement for using memristors in functional circuits is a predictive physical model to capture the resistive switching behavior, which shall be compact enough to be implemented using a circuit simulator. Although a number of memristor models have been developed, most of these models (i.e., first-order memristor models) have utilized only a one-state-variable. However, such simplification is not adequate for accurate modeling because multiple mechanisms are involved in resistive switching. Here, a two-state-variable based second-order memristor model is presented, which considers the axial drift of the charged vacancies in an applied electric field and the radial vacancy motion caused by the thermophoresis and diffusion. In particular, this model emulates the details of the intrinsic short-term dynamics, such as decay and temporal heat summation, and therefore, it accurately predicts the resistive switching characteristics for both DC and AC input signals.

Investigation and Manipulation of Different Analog Behaviors of Memristor as Electronic Synapse for Neuromorphic Applications

Low-power and high-density electronic synapse is an important building block of brain-inspired systems. The recent advancement in memristor has provided an opportunity to advance electronic synapse design. However, a guideline on designing and manipulating the memristor’s analog behaviors is still lacking. In this work, we reveal that compliance current (I_comp) of electroforming process played an important role in realizing a stable analog behavior, which is attributed to the generation of conical-type conductive filament. A proper I_comp could result in a large conductance window, good stability, and low voltage analog switching. We further reveal that different pulse conditions can lead to three analog behaviors, where the conductance changes in monotonic increase, plateau after initial jump, and impulse-like shape, respectively. These behaviors could benefit the design of electronic synapse with enriched learning capabilities. This work will provide a useful guideline for designing and manipulating memristor as electronic synapses for brain-inspired systems.

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.

Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity

A nanoscale, solid-state physically evolving network is experimentally demonstrated, based on the self-organization of Ag nanoclusters under an electric field. The adaptive nature of the network is determined by the collective inputs from multiple terminals and allows the emulation of heterosynaptic plasticity, an important learning rule in biological systems. These effects are universally observed in devices based on different switching materials.

Frequency-dependent learning achieved using semiconducting polymer/electrolyte composite cells

Frequency-dependent learning has been achieved using semiconducting polymer/electrolyte composite cells. The cells composed of polymer/electrolyte double layers realized the conventional spike-rate-dependent plasticity (SRDP) learning model. These cells responded to depression upon low-frequency stimulation and to potentiation upon high-frequency stimulation and presented long-term memory. The transition threshold θm from depression to potentiation varied depending on the previous stimulations. A nanostructure resembling a bio-synapse in its transport passages was demonstrated and a random channel model was proposed to describe the ionic kinetics at the polymer/electrolyte interface during and after stimulations with various frequencies, accounting for the observed SRDP.

Effect of heavy-ion on frequency selectivity of semiconducting polymer/electrolyte heterojunction

Long-term bidirectional frequency selectivity has been achieved in MEH-PPV/PEO–Nd³⁺cells, which suggests spike-rate-dependent plasticity learning protocol. It depends on pulse shape due to variation of ionic type.