Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory

Metaplasticity-Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks

The limitations of deep neural networks in continuous learning stem from oversimplifying the complexities of biological neural circuits, often neglecting the dynamic balance between memory stability and learning plasticity. In this study, artificial synaptic devices enhanced with graphene quantum dots (GQDs) that exhibit metaplasticity is introduced, a higher-order form of synaptic plasticity that facilitates the dynamic regulation of memory and learning processes similar to those observed in biological systems. The GQDs-assisted devices utilize interface-mediated modifications in asymmetric conductive pathways, replicating classical synaptic plasticity mechanisms. This allows for repeatable and linearly programmable adjustments to future weight changes linked to historical weights. Incorporating metaplasticity is essential for achieving generalization within deep neural networks, which enables them to adapt more fluidly to new information while retaining previously acquired knowledge. The GQDs-device-based system achieved a 97% accuracy on the fourth MNIST dataset task, while consistently achieving performance levels above 94% on prior tasks. This performance substantiates the feasibility of directly transferring metaplasticity principles to deep neural networks, thereby addressing the challenges associated with catastrophic forgetting. These findings present a promising hardware solution for developing neuromorphic systems with robust and sustained learning capabilities that can effectively bridge the gap between artificial and biological neural networks.

Optoelectronic neuron based on transistor combined with volatile threshold switching memristors for neuromorphic computing

The human perception and learning heavily rely on the visual system, where the retina plays a vital role in preprocessing visual information. Developing neuromorphic vision hardware is based on imitating the neurobiological functions of the retina. In this work, an optoelectronic neuron is developed by combining a gate-modulated PDVT-10 channel with a volatile threshold switching memristor, enabling the achievement of optoelectronic performance through a resistance-matching mechanism. The optoelectronic spiking neuron exhibits the ability to alter its spiking behavior in a manner resembling that of a retina. Incorporating electrical and optical modulation, the artificial neuron accurately replicates neuronal signal transmission in a biologically manner. Moreover, it demonstrates inhibition of neuronal firing during darkness and activation upon exposure to light. Finally, the evaluation of a perceptron spiking neural network utilizing these leaky integrate-and-fire neurons is conducted through simulation to assess its capability in classifying image recognition algorithms. This research offers a hopeful direction for the development of easily expandable and hierarchically structured spiking electronics, broadening the range of potential applications in biomimetic vision within the emerging field of neuromorphic hardware.

A Temperature Sensory Leaky Integrate-and-Fire Artificial Neuron Based on Chitosan/PNIPAM Bilayer Volatile Complementary Resistive Switching Memristor

The presence of neurons is crucial in neuromorphic computing systems as they play a vital role in modulating the strength of synapses through the release of either excitatory or inhibitory stimuli. Hence, the development of sensory neurons plays a pivotal role in broadening the scope of brain-inspired neural computing. The present study introduces an artificial sensory neuron, which is constructed using a temperature-sensitive volatile complementary resistance switch memristor based on the functional layer of the chitosan/PNIPAM bilayer. The resistive switching behavior arises from the formation and ionization of oxygen vacancy filaments, whereby the threshold voltage and low resistive resistance of the device exhibit a temperature-dependent increase within the range of 290-410 K. A functional replication of a neuron with leaky integration and firing has been successfully developed, effectively simulating essential biological functions such as firing triggered by threshold, refractory period implementation, and modulation of spiking frequency. The artificial sensory neuron exhibits characteristics similar to those of leaky integrated firing neurons that receive temperature inputs. It has the potential to control the output frequency and amplitude under varying temperature conditions, making it suitable for temperature-sensing applications. This study presents a potential hardware implementation for developing efficient artificial intelligence systems that can support temperature detections.

Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks

Compared to artificial neural networks (ANNs), spiking neural networks (SNNs) present a more biologically plausible model of neural system dynamics. They rely on sparse binary spikes to communicate information and operate in an asynchronous, event-driven manner. Despite the high heterogeneity of the neural system at the neuronal level, most current SNNs employ the widely used leaky integrate-and-fire (LIF) neuron model, which assumes uniform membrane-related parameters throughout the entire network. This approach hampers the expressiveness of spiking neurons and restricts the diversity of neural dynamics. In this paper, we propose replacing the resistor in the LIF model with a discrete memristor to obtain the heterogeneous memristive LIF (MLIF) model. The memristance of the discrete memristor is determined by the voltage and flux at its terminals, leading to dynamic changes in the membrane time parameter of the MLIF model. SNNs composed of MLIF neurons can not only learn synaptic weights but also adaptively change membrane time parameters according to the membrane potential of the neuron, enhancing the learning ability and expression of SNNs. Furthermore, since the proper threshold of spiking neurons can improve the information capacity of SNNs, a learnable straight-through estimator (LSTE) is proposed. The LSTE, based on the straight-through estimator (STE) surrogate function, features a learnable threshold that facilitates the backward propagation of gradients through neurons firing spikes. Extensive experiments on several popular static and neuromorphic benchmark datasets demonstrate the effectiveness of the proposed MLIF and LSTE, especially on the DVS-CIFAR10 dataset, where we achieved the top-1 accuracy of 84.40 % .

Atrazine concentration detection based on NiAl-layer double hydroxides nanosheets synaptic transistor

A transistor inspired by biological systems, which possesses synaptic and sensing capabilities, has demonstrated significant promise in the field of neuromorphic electronics and sensory systems resembling the human brain. Despite the remarkable advancements in emulating neuromorphic operations, the development of a synaptic FET with a bionic architecture, extended lifespan, minimal energy usage, and marker monitoring capability remains challenging. In this work, a synaptic transistor based on NiAl-layer double hydroxides nanosheets is reported. The synaptic transistor exhibits a significant ratio of on/off current (1.35×107) and possesses a high transconductance value (10.05 mS). The successful emulation included key synaptic characteristics, such as excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, short-term plasticity spike amplitude-dependent plasticity, spike timing-dependent plasticity, as well as spike number-dependent plasticity. A consumption of 64.8 pJ per spike was achieved as a result of the efficient carrier transfer pathway facilitated by the nanosheets composed of double hydroxides. In addition, the FET's linear detection region (with a coefficient R2=0.811) encompassed atrazine concentrations ranging from 10 pg/mL to 0.1 μg/mL, thanks to its high surface area and significant transconductance. Therefore, this study presents a potential approach for achieving energy-efficient neuromorphic computing and high-performance synaptic devices.

Development of a novel self-healing Zn(II)-metallohydrogel with wide bandgap semiconducting properties for non-volatile memory device application

A rapid and effective strategy has been devised for the swift development of a Zn(II)-ion-based supramolecular metallohydrogel, termed Zn@PEH, using pentaethylenehexamine as a low molecular weight gelator. This process occurs in an aqueous medium at room temperature and atmospheric pressure. The mechanical strength of the synthesized Zn@PEH metallohydrogel has been assessed through rheological analysis, considering angular frequency and oscillator stress dependencies. Notably, the Zn@PEH metallohydrogel exhibits exceptional self-healing abilities and can bear substantial loads, which have been characterized through thixotropic analysis. Additionally, this metallohydrogel displays injectable properties. The structural arrangement resembling pebbles within the hierarchical network of the supramolecular Zn@PEH metallohydrogel has been explored using FESEM and TEM measurements. EDX elemental mapping has confirmed the primary chemical constituents of the metallohydrogel. The formation mechanism of the metallohydrogel has been analyzed via FT-IR spectroscopy. Furthermore, zinc(II) metallohydrogel (Zn@PEH)-based Schottky diode structure has been fabricated in a lateral metal-semiconductor-metal configuration and  it's charge transport behavior has also been studied. Notably, the zinc(II) metallohydrogel-based resistive random access memory (RRAM) device (Zn@PEH) demonstrates bipolar resistive switching behavior at room temperature. This RRAM device showcases remarkable switching endurance over 1000 consecutive cycles and a high ON/OFF ratio of approximately 270. Further, 2 × 2 crossbar array of the RRAM devices were designed to demonstrate OR and NOT logic circuit operations, which can be extended for performing higher order computing operations. These structures hold promise for applications in non-volatile memory design, neuromorphic and in-memory computing, flexible electronics, and optoelectronic devices due to their straightforward fabrication process, robust resistive switching behavior, and overall system stability.

Resistive Memory-Switching Behavior in Solution-Processed Trans, trans-1,4-bis-(2-(2-naphthyl)-2-(butoxycarbonyl)-vinyl) Benzene-PVA-Composite-Based Aryl Acrylate on ITO-Coated PET

Resistive switching memories are among the emerging next-generation technologies that are possible candidates for in-memory and neuromorphic computing. In this report, resistive memory-switching behavior in solution-processed trans, trans-1,4-bis-(2-(2-naphthyl)-2-(butoxycarbonyl)-vinyl) benzene-PVA-composite-based aryl acrylate on an ITO-coated PET device was studied. A sandwich configuration was selected, with silver (Ag) serving as a top contact and trans, trans-1,4-bis-(2-(2-naphthyl)-2-(butoxycarbonyl)-vinyl) benzene-PVA-composite-based aryl acrylate and ITO-PET serving as a bottom contact. The current-voltage (I-V) characteristics showed hysteresis behavior and non-zero crossing owing to voltages sweeping from positive to negative and vice versa. The results showed non-zero crossing in the devices' current-voltage (I-V) characteristics due to the nanobattery effect or resistance, capacitive, and inductive effects. The device also displayed a negative differential resistance (NDR) effect. Non-volatile storage was feasible with non-zero crossing due to the exhibition of resistive switching behavior. The sweeping range was -10 V to +10 V. These devices had two distinct states: 'ON' and 'OFF'. The ON/OFF ratios of the devices were 14 and 100 under stable operating conditions. The open-circuit voltages (Voc) and short-circuit currents (Isc) corresponding to memristor operation were explained. The DC endurance was stable. Ohmic conduction and direct tunneling mechanisms with traps explained the charge transport model governing the resistive switching behavior. This work gives insight into data storage in terms of a new conception of electronic devices based on facile and low-temperature processed material composites for emerging computational devices.

Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing

In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity.

Diverse long-term potentiation and depression based on multilevel LiSiOxmemristor for neuromorphic computing

Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiOx/TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiOxmemristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiOx/TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

Novel ternary organic resistive switching memory doped with bipolar materials

Organic resistive switching memory (ORSM) shows great potential for neotype memory devices due to the preponderances of simple architecture, low power consumption, high switching speed and feasibility of large-area fabrication. Herein, solution-processed ternary ORSM devices doped with bipolar materials were achieved with high ON/OFF ratio and outstanding device stability. The resistive switching performance was effectively ameliorated by doping two bipolar materials (DpAn-InAc and DpAn-5BzAc) in different blending concentration into the PVK:OXD-7 donor-accepter system. Compared with the binary system (PVK: 30 wt% OXD-7), the ON/OFF ratios of the ternary devices doped with 6 wt% DpAn-5BzAc were greatly increased from 7.91 × 10²to 4.98 × 10⁴, with the operating voltage (∣V_set-V_reset∣) declined from 4.90 V to 2.25 V, respectively. Additionally, the stability of resistance state and uniformity of operating voltage were also significantly optimized for the ternary devices. For comparison, ternary devices doped with DpAn-InAc have been explored, which also achieved improved resistive switching behavior. A detailed analysis of electrical characteristics and the internal charge transfer properties of ORSM was performed to unveil the performance enhancement in ternary devices. Results indicate that the use of bipolar materials favors the efficient operation of OSRMs with proper energy level alignment and effective charge transfer.