Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing.

Neuromorphic computing, an emerging computational paradigm, aims to overcome the bottlenecks of the traditional von Neumann architecture. Two-dimensional materials serve as ideal platforms for constructing artificial synaptic devices, yet existing devices based on these materials face challenges such as insufficient stability. Indium selenide (InSe), a two-dimensional semiconductor with unique properties, demonstrates significant potential in the field of neuromorphic devices, though its application research remains in the initial stage. This study presents an artificial synaptic device based on the InSe/Charge Trapping Layer (CTL)/h-BN heterojunction. By applying oxygen plasma treatment to h-BN to form a controllable charge-trapping layer, efficient regulation of carriers in the InSe channel is achieved. The device successfully emulates fundamental synaptic behaviors including paired-pulse facilitation and long-term potentiation/inhibition, exhibiting excellent reproducibility and stability. Through investigating the influence of electrical pulse parameters on synaptic weights, a structure–activity relationship between device performance and structural parameters is established. Experimental results show that the device features outstanding linearity and symmetry, realizing the simulation of key synaptic behaviors such as dynamic conversion between short-term and long-term plasticity. It possesses a high dynamic range ratio of 7.12 and robust multi-level conductance tuning capability, with stability verified through 64 pulse cycle tests. This research provides experimental evidence for understanding interfacial charge storage mechanisms, paves the way for developing high-performance neuromorphic computing devices, and holds broad application prospects in brain-inspired computing and artificial intelligence hardware.

The advent of memristors and the continuing research and development in the field of brain-inspired computing could allow realization of a veritable “thinking machine”. In this study, ZnO-based memristors were fabricated using a radio frequency magnetron sputtering method. The ZnO oxide layer was prepared by incorporating silver nanocrystals (NCs). Several synaptic functions, i.e. nonlinear transmission characteristics, short-term potentiation, long-term potentiation/depression, and pair-pulse facilitation, were imitated in the memristor successfully. Furthermore, the transition from synaptic behaviors to bipolar resistive switching behaviors of the device was also observed under repeated stimulus. It is speculated that the switching mechanism is due to the formation and rupture of the conductive Ag filaments and the corresponding electrochemical metallization. The experimental results demonstrate that the Ag/Ag:ZnO/Pt memristor with resistive switching and several synaptic behaviors has a potential application in neuromorphic computing and data storage systems.

Graphdiyne (GDY) is emerging as a promising material for various applications owing to its unique structure and fascinating properties. However, the application of GDY in electronics and optoelectronics are still in its infancy, primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable applications. Here a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene (Gr) surface, providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse array. Essential synaptic behaviors have been realized, and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault tolerance. Logic functions including “NAND” and “NOR” are integrated into the synapse which can be executed in an optical pathway. Moreover, a visible information sensing-memory-processing system is constructed to execute real-time image acquisition, in situ image memorization and distinction tasks, avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual systems. These results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3/SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5×5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.

Recently, there has been a surge of interest in neuromorphic computation inspired by the extraordinary characteristics of the human brain, such as low energy consumption, parallelism, adaptivity, cognitive abilities, and learning capabilities. Significant research efforts have focused on exploring optical synaptic behaviors in various functional materials. In this study, the potential of red, green and blue (RGB)‐colored long‐persistent luminescence (LPL) in Sm3+/Er3+/La3+‐doped Ca2SnO4 is investigated for synaptic functionality. The luminescence of the samples is continuously enhanced under serial photoexcitation pulse applications, that is, the potentiation process, which is a key feature demonstrated in biological synapses. In addition, multichannel synaptic functionalities in the full‐color range is successfully demonstrated by integrating individual RGB‐colored Sm3+/Er3+/La3+‐doped Ca2SnO4 into a single quantity. To validate the optical synaptic behavior of the samples in neuromorphic computing applications, a reservoir computing (RC) simulation is performed for space‐time data processing using the unique responses of the samples under 4‐bit excitation pulses. The results demonstrated that the multi‐channel synaptic behaviors in the samples should be more valid for utilization in the RC layer than the single channel of synaptic behavior. We suggest this exploration holds promise for the advancement of synaptic devices employing LPL materials.

Materials possessing multiple states are promising to emulate synaptic and neuronic behaviors. Their operation frequency, typically in or below the GHz range, however, limits the speed of neuromorphic computing. Ultrafast THz electric field excitation has been employed to induce nonequilibrium states of matter, called hidden phases in oxides. One may wonder if there are systems for which THz pulses can generate neuronic and synaptic behavior, via the creation of hidden phases. Using atomistic simulations, we discover that relaxor ferroelectrics can emulate all the key neuronic and memristive synaptic features. Their occurrence originates from the activation of many hidden phases of polarization order, resulting from the response of nanoregions to THz pulses. Such phases further possess different dielectric constants, which is also promising for memcapacitor devices.

Organic and perovskite memristors for neuromorphic computing

Artificial synaptic simulating pain-perceptual nociceptor and brain-inspired computing based on Au/Bi3.2La0.8Ti3O12/ITO memristor

Neuromorphic computing offers a promising approach to overcoming the limitations of von Neumann architecture by mimicking biological synapses. While optoelectronic synapses have demonstrated synaptic plasticity through optical and electrical stimuli, most studies rely on ambient light conditions, limiting their robustness and functional complexity. Here, a WSe2/h-BN/SiO2 heterostructure-based optoelectronic synapse is presented that achieves precise synaptic weight modulation through co-stimuli of electrical and optical pulses. The device exhibits enhanced paired-pulse facilitation (PPF) and long-term plasticity (LTP/LTD), demonstrating stable and linear synaptic behavior. Notably, the study systematically analyzes the effects of co-stimuli firing conditions, revealing that both the intensity of light and voltage magnitude influence synaptic weight updates. The device achieves outstanding nonlinearity, high Gmax/Gmin, and stable depression recovery, essential for high-performance neuromorphic computing. Furthermore, ANN-based cognitive simulations using MNIST digits validate their potential for inference tasks, demonstrating near-ideal accuracy. These findings underscore the potential of co-stimuli-driven synapses for multi-modal cognitive systems, paving the way for advanced neuromorphic architectures beyond single-species stimuli constraints.

Flexible transparent InZnO synapse transistor based on Li1.3Al0.3Ti0.7(PO4)3/polyvinyl pyrrolidone nanocomposites electrolyte film for neuromorphic computing

The traditional von Neumann architecture continues to limit the development of artificial intelligence. Memristors have become one of the most promising devices for breaking through the traditional von Neumann architecture. In this work, an optoelectronic synapse based on the CuIn0.7Ga0.3Se2 (CIGS)/ Al-doped ZnO (AZO) p-n heterojunction is prepared by radio-frequency (RF) magnetron sputtering. And the Au/CIGS/AZO/ITO p-n heterojunction artificial synapse has been utilized to simulate various synaptic behaviors as well as the learning-forgetting-relearning process of the human brain. Furthermore, employing a convolutional neural network (CNN) architecture with an enhanced stochastic gradient descent algorithm, the recognition accuracy for the MNIST and Fashion-MNIST datasets is achieved at 97.36% and 83%, respectively, demonstrating the potential application of Au/CIGS/AZO/ITO p-n heterojunction artificial synapse in neuromorphic computing and providing a feasible method for the development of high-performance optoelectronic devices based on CIGS/AZO p-n heterojunctions.

Tuning the properties of magnetic materials by voltage-driven ion migration (magneto-ionics) gives potential for energy-efficient, non-volatile magnetic memory and neuromorphic computing. Here, we report large changes in the magnetic moment at saturation (mS) and coercivity (HC), of 34% and 78%, respectively, in an array of CoFe2O4 (CFO) epitaxial nanopillar electrodes (∼50 nm diameter, ∼70 nm pitch, and 90 nm in height) with an applied voltage of −10 V in a liquid electrolyte cell. Furthermore, a magneto-ionic response faster than 3 s and endurance >2000 cycles are demonstrated. The response time is faster than for other magneto-ionic films of similar thickness, and cyclability is around two orders of magnitude higher than for other oxygen magneto-ionic systems. Using a range of characterization techniques, magnetic switching is shown to arise from the modulation of oxygen content in the CFO. Also, the highly cyclable, self-assembled nanopillar structures were demonstrated to emulate various synaptic behaviors, exhibiting non-volatile, multilevel magnetic states for analog computing and high-density storage. Overall, CFO nanopillar arrays offer the potential to be used as interconnected synapses for advanced neuromorphic computing applications.

Conventional computers excel in logic and accurate scientific calculations butmake hard work of open ended problems that human brains handle easily. Evenvon Neumann—the mathematician and polymath who first developed theprogramming architecture that forms the basis of today's computers—wasalready looking to the brain for future developments before his death in 1957 [1].Neuromorphic computing uses approaches that better mimic the working of thehuman brain. Recent developments in nanotechnology are now providingstructures with very accommodating properties for neuromorphic approaches.This special issue, with guest editors James K Gimzewski and DominiqueVuillaume, is devoted to research at the serendipitous interface between the twodisciplines. 'Synaptic electronics', looks at artificial devices with connections thatdemonstrate behaviour similar to synapses in the nervous system allowing a newand more powerful approach to computing.Synapses and connecting neurons respond differently to incident signalsdepending on the history of signals previously experienced, ultimately leading toshort term and long term memory behaviour. The basic characteristics of asynapse can be replicated with around ten simple transistors. However with thehuman brain having around 1011 neurons and 1015 synapses, artificial neuronsand synapses from basic transistors are unlikely to accommodate the scalabilityrequired. The discovery of nanoscale elements that function as 'memristors' hasprovided a key tool for the implementation of synaptic connections [2].Leon Chua first developed the concept of the 'The memristor—the missing circuitelement' in 1971 [3]. In this special issue he presents a tutorial describing howmemristor research has fed into our understanding of synaptic behaviour and howthey can be applied in information processing [4]. He also describes, 'The newprinciple of local activity, which uncovers a minuscule life-enabling "Goldilockszone", dubbed the edge of chaos, where complex phenomena, including creativityand intelligence, may emerge'.Also in this issue R Stanley Williams and colleagues report results fromsimulations that demonstrate the potential for using Mott transistors as buildingblocks for scalable neuristor-based integrated circuits without transistors [5]. Thescalability of neural chip designs is also tackled in the design reported byNarayan Srinivasa and colleagues in the US [6]. Meanwhile Carsten Timm andMassimiliano Di Ventra describe simulations of a molecular transistor in whichelectrons strongly coupled to a vibrational mode lead to a Franck–Condon (FC)blockade that mimics the spiking action potentials in synaptic memorybehaviour [7].The 'atomic switches' used to demonstrate synaptic behaviour by acollaboration of researchers in California and Japan also come under furtherscrutiny in this issue. James K Gimzewski and colleagues consider the differencebetween the behaviour of an atomic switch in isolation and in a network [8]. Asthe authors point out, 'The work presented represents steps in a unified approachof experimentation and theory of complex systems to make atomic switchnetworks a uniquely scalable platform for neuromorphic computing'. Researchersin Germany [9] and Sweden [10] also report on theoretical approaches to modelling networks of memristive elements and complementary resistiveswitches for synaptic devices.As Vincent Derycke and colleagues in France point out, 'Actual experimentaldemonstrations of neural network type circuits based on non-conventional/non-CMOS memory devices and displaying function learning capabilities remain veryscarce'. They describe how their work using carbon nanotubes provides a raredemonstration of actual function learning with synapses based on nanoscalebuilding blocks [11]. However, this is far from the only experimental workreported in this issue, others include: short-term memory of TiO2-basedelectrochemical capacitors [12]; a neuromorphic circuit composed of a nanoscale1-kbit resistive random-access memory (RRAM) cross-point array of synapsesand complementary metal–oxide–semiconductor (CMOS) neuron circuits [13]; aWO3−x-based nanoionics device from Masakazu Aono's group with a wide scaleof reprogrammable memorization functions [14]; a new spike-timing dependentplasticity scheme based on a MOS transistor as a selector and a RRAM as avariable resistance device [15]; a new hybrid memristor-CMOS neuromorphiccircuit [16]; and a photo-assisted atomic switch [17]. Synaptic electronicsevidently has many emerging facets, and Duygu Kuzum, Shimeng Yu, andH-S Philip Wong in the US provide a review of the field, including the materials,devices and applications [18].In embracing the expertise acquired over thousands of years of evolution,biomimetics and bio-inspired design is a common, smart approach totechnological innovation. Yet in successfully mimicking the physiologicalmechanisms of the human mind synaptic electronics research has a potentialimpact that is arguably unprecedented. That the quirks and eccentricities recentlyunearthed in the behaviour of nanomaterials should lend themselves soaccommodatingly to emulating synaptic functions promises some very excitingdevelopments in the field, as the articles in this special issue emphasize.

Field effect transistors based on 2D layered material have gained significant potential in emerging technologies, such as neuromorphic computing and ultrafast memory response for artificial intelligence applications. This study proposes a facile approach to fabricate an optoelectronic artificial synapse for neuromorphic computing and light‐decoding information system by utilizing the 2D heterostructure of CrSBr/PtS2 to overcome circuit complexity. The CrSBr layer serves as a trapping layer, while PtS2, mounted on top of CrSBr, acts as a channel layer. PtS2 exhibits n‐type semiconductor behavior with a hysteresis that varies with the thickness of the underlying CrSBr layer. The heterostructure device, featuring a 96.3 nm thick CrSBr layer, exhibited a large memory window of 11.9 V when the gate voltage is swept from −10 V to +10 V. Various synaptic behaviors are effectively demonstrated, including paired‐pulse facilitation, excitatory postsynaptic current, optical spike number and intensity‐dependent plasticity using laser light at a wavelength of 365 nm. The device achieves 26 distinct output signals depending on the intensity of the incident laser light, ranging from 10 to 385 mW cm−2, enabling its applications for light‐decoded information security systems. Thus, the investigation presents a unique approach to artificial intelligence and cybersecurity systems.