Paired-pulse Facilitation Research Articles

Improvement of volatile switching in scaled silicon nanofin memristor for high performance and efficient reservoir computing.

Conductive-bridge random access memory can be used as a physical reservoir for temporal learning in reservoir computing owing to its volatile nature. Herein, a scaled Cu/HfOx/n+-Si memristor was fabricated and characterized for reservoir computing. The scaled, silicon nanofin bottom electrode formation is verified by scanning electron and transmission electron microscopy. The scaled device shows better cycle-to-cycle switching variability characteristics compared with those of large-sized cells. In addition, synaptic characteristics such as conductance changes due to pulses, paired-pulse facilitation, and excitatory postsynaptic currents are confirmed in the scaled memristor. High-pattern accuracy is demonstrated by deep neural networks applied in neuromorphic systems in conjunction with the use of the Modified National Institute of Standards and Technology database. Furthermore, a reservoir computing system is introduced with six different states attained by adjusting the amplitude of the input pulse. Finally, high-performance and efficient volatile reservoir computing in the scaled device is demonstrated by conductance control and system-level reservoir computing simulations.

Centimeter-Scale Tellurium Oxide Films for Artificial Optoelectronic Synapses with Broadband Responsiveness and Mechanical Flexibility.

Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain's operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.

Metal-organic framework and MXene (ZIF-8:Ti3C2Tx) based organic and inorganic nanocomposite for bio-synaptic applications

Organic and inorganic hybrid materials have drawn more interest in the field of electronic devices. The metal-organic frameworks (MOFs) have emerged as one of the promising hybrid materials for electronic devices. However, their limited electrical conductivity hinders their electronic applications. To overcome these shortcomings, we synthesized MOF:MXene nanocomposite to enhance the stability and electrical conductivity of MOF. In particular, we synthesized and characterized zeolitic imidazolate framework-8 (ZIF-8):MXene (Ti3C2Tx) hybrid nanocomposite. From the application point of view, we demonstrated the analog memory effect and mimicked various synaptic learning functionalities using Ag/ZIF-8:Ti3C2Tx/FTO resistive switching (RS) device. The fabricated device exhibits a good analog-type bipolar RS effect. The device mimics various bio-synaptic properties such as potentiation, depression, excitatory-post synaptic current, and paired-pulse facilitation (PPF) index (%). The inherent non-ideal memristor property was dominated in the device, owing to the double-valued charge-flux relation. The conduction and possible RS mechanisms of the Ag/ZIF-8:Ti3C2Tx/FTO device were reported. These results suggested that the ZIF-8:Ti3C2Tx nanocomposite is a potential nanomaterial for brain-inspired computing applications.

Optoelectronic Synapse Behaviors in Tb3+ and Al3+ Co-Doped CaSnO3 with Long-Persistent Luminescence.

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo-synaptic behavior in the field of rare earth ion-doped luminescent oxides by using long-persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic-based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV-light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired-pulse facilitation, pulse-number/timing dependent potentiation, and pulse-number/timing dependent short- to long-term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli.

Spike-enhanced synapse functions of SnOx-based resistive memory

This paper explores the application of resistive random-access memory (RRAM) devices in emulating various synapse functions. The electrical properties of an indium tin oxide (ITO)/SnOx/TaN device are examined, including the activation process, switching process, endurance (>102), and retention characteristics (104 s). A conduction mechanism for the resistive-switching process based on the migration of oxygen ions in the insulating SnOx film is proposed. Furthermore, the multilevel cell characteristics of the device, demonstrating the control of resistance states by variations in reset voltage and compliance current, were found to be suitable, increasing its data storage capacity. Synapse functions, including potentiation and depression, are tested, and their repeatability is evaluated. A neural network-based Modified National Institute Standards and Technology pattern recognition system is applied to the ITO/SnOx/TaN device, showcasing its capabilities in pattern recognition tasks. This study further explores various synapse functions, such as paired-pulse facilitation, paired-pulse depression, excitatory postsynaptic current, and activity-dependent synaptic plasticity. Hebbian learning rules, including spike rate-dependent plasticity, are demonstrated, along with spike time-dependent plasticity under various spike types, making it suitable for high-functionality neuromorphic applications. Finally, we assembled 3 × 3 synapse arrays and utilized them to form recognizable patterns, demonstrating the potential of the device in implementing neural network functions.

Double perovskite Bi2FeMnO6/TiO2 thin film heterostructure device for neuromorphic computing

Multiferroic materials have important research significance in the fields of magnetic random-access memory, ferroelectric random-access memory, resistive random-access memory, and neuromorphic computing devices due to their excellent and diverse physical properties. In this work, a solution of Bi2FeMnO6 was prepared using a solution-based method, and an Au/Bi2FeMnO6/TiO2 heterostructure device was fabricated on a Si substrate. X-ray diffraction and transmission electron microscopy data indicate that the Bi2FeMnO6 films have hexagonal R3c symmetry structures. The Bi2FeMnO6 film exhibits ferroelectricity with a fine remanent polarization. In addition, the Bi2FeMnO6-based devices have excellent switching ratios of 6.37 × 105. A larger switching ratio can provide a multi-resistance state for the device, which is beneficial for the simulation of synapses. Hence, it effectively emulates excitatory postsynaptic currents, paired-pulse facilitation, and long-term plasticity of synapses and achieves recognition accuracy of 95% in neuromorphic computing. We report a promising material for the development of various nonvolatile memories and neuromorphic synaptic devices.

Skin-like n-type stretchable synaptic transistors with low energy consumption and highly reliable plasticity for brain-inspired computing

Artificial synaptic devices with high transconductance, low energy consumption and good stretchability are important for efficient and energy-friendly neuromorphic computations in the field of bionics. Here, we propose a stretchable substrate inspired by wrinkled surface of human skin, and achieve a low energy operation stretchable synaptic transistor (SST) based on n-type oxide semiconductor. The SSTs exhibit excellent performance, including high mobility (4.08 cm2V−1s−1) and high transconductance (over 12 mS) under ultra-low operation voltage of 0.1 V. They can stand against multi-directional stretching of 10 % and up to 400 stretch/release cycles. In addition, the n-type SSTs achieve synaptic performance at ultra-low energy consumption (0.36 fJ) with dual-mode operation characteristics of excitatory and inhibitory behaviors. Under tensile strain, they exhibit typical synaptic behavior, including short-term/long-term plasticity, pair pulse facilitation, spike voltage/frequency/duration/number-dependent plasticity. More importantly, the SSTs exhibit excellent “learning-forgetting-relearning” feature and good stability under potentiation-depression cycle test, showcasing tremendous potential in brain-inspired computation. Finally, a high recognition accuracy (89.6 %) is attained simulated by handwritten digital datasets. To the best of our knowledge, this is the first stretchable synaptic transistor with oxide semiconductors, and it is also a major breakthrough in n-type stretchable synaptic transistors for high-speed and low-energy calculation and storage for brain-inspired computing.

Design of gelatin-based bionic device for neural computing applications

This study introduces an approach to the development of resistive random-access memory (RRAM) devices by utilizing gelatin-based materials and incorporating an ultrathin Al2O3 interface layer fabricated using atomic layer deposition (ALD). The integration of the ALD technology for Al2O3 deposition ensures precise control over the layer thickness and quality, contributing to enhanced device uniformity and stability. The AGAI device can withstand more than 200 switching cycles while maintaining a high ON/OFF ratio exceeding 103, and it also successfully emulates synaptic plasticity characteristics, including long-term synaptic plasticity and short-term synaptic plasticity, as demonstrated by their responses to paired-pulse facilitation and paired-pulse depression, respectively, thereby validating their potential for neural computing applications. Additionally, the hybrid conduction mechanism in the AGAI device, involving aluminum and oxygen vacancy filaments, provides remarkable control over the switching process, leading to repeatability in synaptic characteristics and resistance states. These capabilities introduce new possibilities for the development of neuromorphic computing systems, which aim to mimic the functionality of the human brain in processing and storing information.

Performance improvement of bilayer memristor based on hafnium oxide by Ti/W synergy and its synaptic behavior

Bilayer and multi-layer structures can effectively improve the performance of memristors, but the oxide/metal interface during the resistance switching process affects its reliability and stability. In this work, the Pt/HfO2/HfOx/W and Pt/HfO2/HfOx/Ti/W memristors with a homogeneous bilayer structure are compared to systematically investigate the effect of interface engineering on bilayer hafnium oxide devices by inserting Ti buffer layer to construct Ti/W composite electrode. Compared with the stimulated oxidation of the W electrode, the device under the synergy of Ti/W composite electrode showed better switching uniformity (The resistance of the high-resistance state and reset voltage variation coefficient decreased from 83.1 % to 31.9 % and from 9.5 % to 5.6 %, respectively.) and robust endurance (>104 cycles). Meanwhile, some essential synaptic behaviors such as nonlinear transmission characteristics, paired-pulse facilitation, and spike-timing-dependent plasticity were mimicked by using different pulse stimuli. Subsequently, the resistance switching mechanism of the device under the Ti/W synergy was explored by combining the results of I–V curve fitting and material analysis. These results provided important experimental guidance and a theoretical basis for further optimization of electrode interfacial effect in the future.

Artificial synapses based on P(VDF-TrFE-CTFE)/sodium alginate heterojunction memristor for distance detection application

The utilization of heterogeneous architecture presents a promising approach to bolster the reliability of memristors and achieve high-density memory with synaptic properties. The preparation of P(VDF-TrFE-CTFE)/sodium alginate heterojunction memristors was accomplished through the rotary coating method. The investigation was conducted on the electrical properties and synaptic behavior of the heterojunction memristors. The memristor exhibits the potential to emulate crucial synaptic behaviors, such as paired-pulse facilitation, long-term potentiation, long-term depression, excited postsynaptic current and inhibitory postsynaptic current, as well as learning behavior. This highlights its prospective applicability in neuromorphic devices. A distance sensing system is established utilizing the P(VDF-TrFE-CTFE)/sodium alginate heterojunction memristor artificial synaptic device in conjunction with the Al/sodium alginate/ITO threshold memristor artificial neuron. The detection of distance is achieved through the transmission of signals from the synaptic device and triggering of thresholds in the threshold device. This system enables distance detection ranging from 0.5 cm to 14 cm. This research is crucial for advancing the development of biomimetic sensing systems and facilitating the utilization of memristors in the field of sensing.

Clarify the contribution of shallow and deep interfacial traps to the transistor-type optoelectronic synaptic device

Recently, transistor-type optoelectronic synaptic device has garnered widespread attention due to its high potential for realizing artificial visual systems and neuromorphic computing. However, some basic mechanisms, such as the contribution of shallow and deep traps on the synaptic behavior, are still not fully understood. In this work, a channel-only transistor-type optoelectronic synaptic device has been employed as platform to clarify the contribution of shallow and deep traps to the synaptic response. We firstly demonstrated that the channel-only transistor-type synaptic device can successfully mimic almost all synaptic behavior, such as excitatory postsynaptic current spike (ΔEPSC), paired-pulse facilitation (PPF), etc. And then, two individual double-exponential models have been employed to fit the generation and decay part of ΔEPSC response to distinguish the role of shallow and deep traps on the synaptic behavior. The results suggest that the shallow trap gives rise to the fast response in both generation and decay component, and the deep trap contributes to the slow component in both generation and decay part. In addition, the number of deep traps is critical to determine the metastable current in the decay part because of the photogating effect. This explanation has been further confirmed by increasing the number of electrons that can be captured by increasing the light pulse intensity, and tuning the number of trap sites by storing the synaptic device in ambient environment or functionalizing the SiO2 surface with SAM agent containing strong electron withdrawing end group. Thus, this work not only clarify the contribution of shallow and deep traps, but also provide several strategies to tune the synaptic behavior.

Investigation of Resistance Switching and Synaptic Properties of VO x for Neuromorphic Applications.

The taking run on artificial intelligence in the last decades is based on the von Neumann architecture where memory and computation units are separately located from each other. This configuration causes a large amount of energy and time to be dissipated during data transfer between these two units, in contrast to synapses in biological neurons. A new paradigm has been proposed inspired by biological neurons in human brains, known as neuromorphic computing. Due to the unusual current-voltage characteristic of memristor devices such as pinched hysteresis loops, memristors are considered a key element of neuromorphic architecture. In this study, we report the basic current-voltage characteristic of the memristor devices in the form of Si/SiO2/Pt(30 nm)/VO x (3, 13, 25 nm)/Pt (30 nm) sandwich structure. Synaptic functions such as spike-time-dependent plasticity (STDP), paired-pulse facilitation (PPF), long-term potentiation (LTP), and long-term depression (LTD) of memristor devices were examined in detail. The oxide layer VO x has been grown by using the VO2 target in a pulsed laser deposition (PLD) chamber. The composition and oxidation states of the oxide layer were examined using the X-ray photoelectron spectroscopy (XPS) technique. The status of oxygen vacancies, which play an active role in the operation of the devices, was examined with a photoluminescence (PL) technique. The experimental results showed that the thickness of the oxide layer can significantly influence the synaptic and resistive switching properties of the devices.

A synapse with low power consumption based on MoTe2/SnS2 heterostructure

The use of two-dimensional materials and van der Waals heterostructures holds great potential for improving the performance of memristors Here, we present SnS2/MoTe2 heterostructure synaptic transistors. Benefiting from the ultra-low dark current of the heterojunction, the power consumption of the synapse is only 19 pJ per switching under 0.1 V bias, comparable to that of biological synapses. The synaptic device based on the SnS2/MoTe2 demonstrates various synaptic functionalities, including short-term plasticity, long-term plasticity, and paired-pulse facilitation. In particular, the synaptic weight of the excitatory postsynaptic current can reach 109.8%. In addition, the controllability of the long-term potentiation and long-term depression are discussed. The dynamic range (G max/G min) and the symmetricity values of the synaptic devices are approximately 16.22 and 6.37, and the non-linearity is 1.79. Our study provides the possibility for the application of 2D material synaptic devices in the field of low-power information storage.

Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems.

The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.

A pure pyrochlore phase ferroelectric thin film diode for optoelectric artificial synapse

In this work, Au/Bi2Ti2O7/ITO ferroelectric diodes based on Schottky barrier were fabricated on ITO substrates by the sol-gel method. The device has a simple structure and good diode performance. Under UV light irradiation at 23.5 mW/cm2, the device exhibits an open-circuit voltage of 0.31 V and a short-circuit current of −0.0425 μA. It shows that the device has good optoelectronic properties. Au/Bi2Ti2O7/ITO ferroelectric diodes also exhibited behaviors similar to biological synapses, including long-term plasticity, short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity when stimulated by pulses. We also build convolutional neural networks and use the MNIST dataset for handwritten digit recognition.

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems.

In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

High sensitivity artificial synapses using printed high-transmittance ITO fibers for neuromorphic computing

Artificial synapses are essential building blocks for neuromorphic electronics. Here, solid polymer electrolyte-gated artificial synapses (EGASs) were fabricated using ITO fibers as channels, which possess an ultra-high sensitivity of 5 mV and a long-term memory time exceeding 3 min. Notably, digitally printed ITO-fiber arrays exhibit an ultra-high transmittance of approximately 99.67 %. Biological synaptic plasticity, such as excitatory postsynaptic current, paired-pulse facilitation, spike frequency-dependent plasticity, and synaptic potentiation and depression, were successfully mimicked using the EGASs. Based on the synaptic properties of the EGASs, an artificial neural network was constructed to perform supervised learning using the Fashion-MNIST dataset, achieving high pattern recognition rate (82.39 %) due to the linear and symmetric synaptic plasticity. This work provides insights into high-sensitivity artificial synapses for future neuromorphic computing.

Light-Induced Conductance Potentiation and Depression in an All-Optically Controlled Memristor.

Optoelectronic memristors are new multifunctional devices with both electrically tunable and light-tunable synaptic plasticity, attracting great attention as key promising devices for optoelectronic neuromorphic computing systems. At present, the conductance modulation in most optoelectronic memristors is conducted in a hybrid photoelectric mode, suffering some problems such as heat generation and control complexity. Here, an optoelectronic memristor based on the p+-Si/n-ZnO heterojunction is proposed where the conductance can be reversibly modulated in an all-optically controlled mode. The electron detrapping/trapping mechanism at the p+-Si/n-ZnO interface barrier region is presented to explain the light-induced conductance potentiation/depression behavior. Furthermore, some synaptic functions, including excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), and paired-pulse facilitation (PPF), are successfully mimicked in the p+-Si/n-ZnO heterojunction memristor, instructing its application potential for optoelectronic neuromorphic computing.

Highly uniform organic nanowire synaptic arrays with excellent performance for associative memory

Synaptic devices of optoelectronic nature, which combine light-detection and data-storage capabilities, hold significant promise in the realm of neuromorphic computing. They are especially beneficial for the processing of visual data and the execution of intricate cognitive functions akin to learning, memory retention, and logical reasoning. However, current research is mostly confined to the level of individual devices, with corresponding studies on synaptic arrays being relatively scarce. Type II heterojunctions are widely used in optoelectronics. Bandgap engineering enables efficient separation and trapping of photogenerated excitons in selected materials, reducing carrier recombination. This prolongs carrier retention in the channel, delaying photocurrent decay, crucial for artificial optoelectronic synapses. We designed an organic nanowire/perovskite Type II heterojunction where CsPbBr3 perovskite films and TIPS nanowires serve as the photosensitive and channel layers, respectively. The composite synaptic array was successfully fabricated by in-situ growth of TIPS nanowires on the surface of perovskite thin films. It was further used to fabricate a photonic synapse array that exhibits characteristic photocurrent and stable optical response. Achieving high-performance photonic synapses was facilitated by characteristics such as high mobility and high on/off ratios, stemming from the formation of complete single-crystal nanowires on the perovskite films. Exhibiting synaptic behaviors such as photo-induced enhancements and paired-pulse facilitation, the device enabled a visual sensing system with a 5 × 5 pixel array for simulating memory processes and restoring associative memories. A novel photocurrent model was established for understanding device characteristics. This work provides valuable insights for future utilization of composite organic nanowire arrays in simulating brain-like computations and realizing large-scale intelligent visual sensing systems.

Role of Ti interfacial layer in the stability of TiO2 based transparent synaptic device

Interface engineering plays a pivotal role in manipulating the electrical transport and conduction mechanism of a synaptic device. In this work, the impact of Ti as an interfacial layer is systematically investigated by inserting ∼5 nm thin film at both interfaces of a functional layer (TiO2). Interestingly, it was observed that Ti layers significantly regulate the migration of oxygen ions/vacancies at the interfaces yielding an improved stability from 10 to 200 cycles, sustained over a longer period ∼8 × 103 s. Forming free and gradual transition in conductance on positive bias region under a controlled compliance current ∼0.3–17 mA demonstrates the multilevel switching highlighting the typical synaptic behavior of memristor. The ohmic conduction and space charge-limited current mechanism was found across the various resistive states signifies the trapping/de-trapping. Besides, other key parameters of synaptic device such as paired pulse facilitation, depression, and short-term memory together with the excellent transmittance in the visible spectral range makes our device adequate for innovative transparent neuromorphic applications.