A carbon-based memristor design for associative learning activities and neuromorphic computing

Boron-Doped Engineering for Carbon Quantum Dots-Based Memristors with Controllable Memristance Stability

Carbon quantum dots-based memristors (CQDMs) have emerged as a rising star in data storage and computing. The key constraint to their commercialization is memristance variability, which mainly arises from the disordered conductive paths. Doping methodology can optimize electron and ion transport to help construct a stable conductive mode. Herein, based on boron (B)-doped engineering strategy, three kinds of comparable quantum dots are synthesized, including carbon quantum dots (CQDs), a series of boron-doped CQDs (BCQDs) with different B contents, and boron quantum dots. The corresponding device performances highlight the superiority of BCQDs-based memristors, exhibiting a ternary flash-type memory behavior with longer retention time and more controllable memristance stability. The comprehensive analysis results, including device performance, functional layer morphology, and material simulated calculation, illustrate that the doped B elements can directionally guide the migration of aluminum ions by enhancing the capture of free electrons, resulting in ordered conductive filaments and stable ternary memory behavior. Finally, the conceptual applications of logic display and logic gate are discussed, indicating a bright prospect for BCQDs-based memristors. This work proves that modest B doping can optimize memristance property, establishing a theoretical foundation and template scheme for developing effective and stable CQDMs.

Emulating learning behavior in a flexible device with self-formed Ag dewetted nanostructure as active element

Neuromorphic devices are a promising alternative to the traditional von Neumann architecture. These devices have the potential to achieve high-speed, efficient, and low-power artificial intelligence. Flexibility is required in these devices so that they can bend and flex without causing damage to the underlying electronics. This feature shows a possible use in applications that require flexible electronics, such as robotics and wearable electronics. Here, we report a flexible self-formed Ag-based neuromorphic device that emulates various brain-inspired synaptic activities, such as short-term plasticity and long-term potentiation (STP and LTP) in both the flat and bent states. Half and full-integer quantum conductance jumps were also observed in the flat and bent states. The device showed excellent switching and endurance behaviors. The classical conditioning could be emulated even in the bent state.

Silicon based Bi0.9La0.1FeO3 ferroelectric tunnel junction memristor for convolutional neural network application

Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

Hybridization state transition-driven carbon quantum dot (CQD)-based resistive switches for bionic synapses

As an emerging carbon-based material, carbon quantum dots (CQDs) have shown unstoppable prospects in the field of bionic electronics with their outstanding optoelectronic properties and unique biocompatible characteristics. In this study, a novel CQD-based memristor is proposed for neuromorphic computing. Unlike the models that rely on the formation and rupturing of conductive filaments, it is speculated that the resistance switching mechanism of CQD-based memristors is due to the conductive path caused by the hybridization state transition of the sp² carbon domain and sp³ carbon domain induced by the reversible electric field. This avoids the drawback of uncontrollable nucleation sites leading to the random formation of conductive filaments in resistive switching. Importantly, it illustrates that the coefficient of variation (C_V) of the threshold voltage can be as low as -1.551% and 0.083%, which confirms the remarkable uniform switching characteristics. Interestingly, the Pavlov's dog reflection as an important biological behavior can be demonstrated by the samples. Finally, the accuracy recognition rate of MNIST handwriting can reach up to 96.7%, which is very close to the ideal number (97.8%). A carbon-based memristor based on a new mechanism presented provides new possibilities for the improvement of brain-like computing.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film

The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.

Forgetting memristor based STDP learning circuit for neural networks

The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. This paper proposes a new STDP learning rule implementation circuit based on the forgetting memristor. This kind of forgetting memory resistance synapse makes the neural network have the function of time-division multiplexing, but the instability of short-term memory will affect the learning ability of the neural network. This paper analyzes and discusses the influence of synapses with long-term and short-term memory on the learning characteristics of neural network STDP, which lays a foundation for the construction of time-division multiplexing neural network with long-term and short-term memory synapses. Through this circuit, it is found that the volatile memristor has different behaviors to the stimulus signal in different initial states, and the resulting LTP phenomenon is more in line with the forgetting effect in biology. This circuit has multiple adjustable parameters, which can fit the STDP learning rules under different conditions. The application of neural network proves the availability of this circuit.

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

Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.

Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing

As the emerging member of zero-dimension transition metal dichalcogenide, WSe₂ quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe₂ QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe₂ QDs/La_0.3Sr_0.7MnO₃/SrTiO₃. The device displays excellent resistive switching memory behavior with a R_OFF/R_ON ratio of ~5 × 10³, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe₂ QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.

Bi2O2Se-based integrated multifunctional optoelectronics

The prominent light-matter interaction in 2D materials has become a pivotal research area that involves either an archetypal study of inherent mechanisms to explore such interactions or specific applications to assess the efficacy of such novel phenomena. With scientifically controlled light-matter interactions, various applications have been developed. Here, we report four diverse applications on a single structure utilizing the efficient photoresponse of Bi₂O₂Se with precisely tuned multiple optical wavelengths. First, the Bi₂O₂Se-based device performs the function of optoelectronic memory using UV (λ = 365 nm, 1.1 mW cm^-2) for the write-in process with SiO₂ as the charge trapping medium followed by a +1 V bias for read-out. Second, associative learning is mimicked with wavelengths of 525 nm and 635 nm. Third, using similar optical inputs, functions of logic gates "AND", "OR", "NAND", and "NOR" are realized with response current and resistance as outputs. Fourth is the demonstration of a 4 bit binary to the decimal converter using wavelengths of 740 nm (LSB), 595 nm, 490 nm, and 385 nm (MSB) as binary inputs and output response current regarded as equivalent decimal output. Our demonstration is a paradigm for Bi₂O₂Se-based devices to be an integral part of future advanced multifunctional electronic systems.

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.

Electrically and Optically Controllable p-n Junction Memtransistor Based on an Al2 O3 Encapsulated 2D Te/ReS2 van der Waals Heterostructure

The exploration of memtransistors as a combination of a memristor and a transistor has recently attracted intensive attention because it offers a promising candidate for next-generation multilevel nonvolatile memories and synaptic devices. However, the present state-of-the-art memtransistors, which are based on a single material, such as MoS₂ or perovskite, exhibit a relatively low switching ratio, require extremely high electric fields to modulate bistable resistance states and do not perform multifunctional operations. Here, the realization of an electrically and optically controllable p-n junction memtransistor using an Al₂ O₃ encapsulated 2D Te/ReS₂ van der Waals heterostructure is reported. The hybrid memtransistor shows a reversible bipolar resistance switching behavior between a low resistance state and a high resistance state with a high switching ratio up to 10⁶ at a low operating voltage (<10 V), high cycling endurance, and long retention time. Moreover, multiple resistance states are achieved by applying different bias voltages, gate voltages, or light powers. In addition, logical operations, including the inverter and AND/OR gates, and synaptic activities are performed by controlling the optical and electrical inputs. The work offers a novel strategy for the reliable fabrication of p-n junction memtransistors for multifunctional devices and neuromorphic applications.

ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing

The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO₃ multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO₃ multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO₃-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO₃-based memristive application in artificial intelligence.

Fabrication and investigation of quaternary Ag-In-Zn-S quantum dots-based memristors with ultralow power and multiple resistive switching behaviors

Quaternary Ag-In-Zn-S (AIZS) quantum dots (QDs) play critical roles in various applications since they have advantages of combining superior optical and electrical features, such as tunable fluorescence emission and high carrier mobilities. However, the application of semiconductor AIZS QDs in brain-inspired devices (e.g. memristor) has been rarely reported. In this work, the tunable volatile threshold switching (TS) and non-volatile memory switching (MS) behaviors have been obtained in a memristor composed of AIZS QDs by regulating the magnitude of compliance current. Additionally, the innovative Ag/AIZS structure devices without traditional oxide layer exhibit low operation voltage (∼0.25 V) and programming current (100 nA) under the TS mode. Moreover, the devices achieve reproducible bipolar resistive switching (RS) behaviors with large ON/OFF ratio of ∼10⁵, ultralow power consumption of ∼10^-10 W, and good device-to-device uniformity under the MS mode. Furthermore, the charge transport mechanisms of the high- and low-resistance states under the positive and negative bias have been analyzed with space-charge-limited-current and filament conduction models, respectively. This work not only validates the potential of AIZS QDs acting as dielectric layer in RS devices but also provides a new guideline for designing ultralow power and multiple RS characteristics devices.