Atomic Switch Research Articles

Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks

AbstractThe synaptic plasticity of the Ag‐Ag2S nanoparticle‐based volatile memristor system is demonstrated. The nanoparticles self‐assemble into a network with over 103 interconnected atomic switch interfaces. Short‐term plasticity is identified by spontaneous conductance relaxation, attributed to the memristor's volatility. The conductance of the network is enhanced when a subsequent stimulus pulse arrives shortly after the previous one, analogous to the paired‐pulse facilitation in biological synapses. Furthermore, repeated pulse stimulation is used to achieve the transition from short‐term plasticity to long‐term potentiation, a process related to learning and memory formation. Remarkably, the result reveals that the lifetime of long‐term potentiation for 100‐pulse stimulation is 40 min, indicating that the device can forget newly acquired information after prolonged storage, akin to human memories. The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.

Reversible Crystalline‐Crystalline Transitions in Chalcogenide Phase‐Change Materials

AbstractPhase‐change random access memory (PCRAM) is one of the most technologically mature candidates for next‐generation non‐volatile memory and is currently at the forefront of artificial intelligence and neuromorphic computing. Traditional PCRAM exploits the typical phase transition and electrical/optical contrast between non‐crystalline and crystalline states of chalcogenide phase‐change materials (PCMs). Currently, traditional PCRAM faces challenges that vastly hinder further memory optimization, for example, the high‐power consumption, significant resistance drift, and the contradictory nature between crystallization speed and thermal stability, nearly all of them are related to the non‐crystalline state of PCMs. In this respect, a reversible crystalline‐to‐crystalline phase transition can solve the above problems. This review delves into the atomic structures and switching mechanisms of the emerging atypical crystalline‐to‐crystalline transitions, and the understanding of the thermodynamic and kinetic features. Ultimately, an outlook is provided on the future opportunities that atypical all‐crystalline phase transitions offer for the development of a novel PCRAM, along with the key challenges that remain to be addressed.

Volatile and Nonvolatile Dual‐Mode Switching Operations in an Ag‐Ag2S Core‐Shell Nanoparticle Atomic Switch Network

AbstractThis paper proposes a nanoparticle‐based atomic switch network memristive device, capable of both volatile and nonvolatile switching operations, which have not been previously reported for this material. The operational modes can be determined by altering the compliance current, demonstrating high stability over 100 cycles. Analysis of the conduction mechanism using I–V curves reveals switching characteristics consistent with space‐charge‐limited current conduction during the set process and ohmic behavior in the reset state. Furthermore, this study analyzes these dual‐operational modes in devices with varying electrode spacings. The results indicate that a wider spacing necessitated a higher compliance current for the volatile‐to‐nonvolatile transition, underscoring the significance of interconnection. These findings facilitate the integration of neuron and synapse functions within a single atomic switch network device, thereby advancing neuromorphic systems.

Rutaecarpine-inspired scaffold-hopping strategy and Ullmann cross-coupling based synthetic approach: Identification of pyridopyrimidinone-indole based novel anticancer chemotypes

Natural products as starting templates have shown historically major contribution to development of drugs. Inspired by the structure–function of an anticancer natural alkaloid Rutaecarpine, the Scaffold-hopped Acyclic Analogues of Rutaecarpine (SAAR) with ‘N’-atom switch (1°-hop) and ring-opening (2°-hop) were investigated. A new synthetic route was developed for an effective access to the analogues, i.e. 2-indolyl-pyrido[1,2-a]pyrimidinones, which involved preparation of N-Boc-N’-phthaloyltryptamine/mexamine-bromides and pyridopyrmidinon-2-yl triflate, a nickel/palladium-catalysed Ullmann cross-coupling of these bromides and triflate, deprotection of phthalimide followed by N-aroylation, and Boc-deprotection. Fourteen novel SAAR-compounds were prepared, and they showed characteristic antiproliferative activity against various cancer cells. Three most active compounds (11a, 11b, and 11c) exhibited good antiproliferative activity, IC50 7.7–15.8 µM against human breast adenocarcinoma cells (MCF-7), lung cancer cells (A549), and colon cancer cells (HCT-116). The antiproliferative property was also observed in the colony formation assay. The SAAR compound 11b was found to have superior potency than original natural product Rutaecarpine and an anticancer drug 5-FU in antiproliferative activities with relatively lower cytotoxicity towards normal breast epithelial cells (MCF10A) and significantly higher inhibitory effect on cancer cells’ migration. The compound 11b was found to possess favourable in silico physicochemical characteristics (lipophilicity-MLOGP, TPSA, and water solubility-ESOL, and others), bioavailability score, and pharmacokinetic properties (GI absorption, BBB non-permeant, P-gp, and CYP2D6). Interestingly, the compound 11b did not show any medicinal chemistry structural alert of PAINS and Brenk filter. The study represents for the first time the successful discovery of new potent anticancer chemotypes using Rutaecarpine natural alkaloid as starting template and reaffirms the significance of natural product-inspired scaffold-hopping technique in drug discovery research.

Toward Practical Single-Molecule/Atom Switches.

Electronic switches have been considered to be one of the most important components of contemporary electronic circuits for processing and storing digital information. Fabricating functional devices with building blocks of atomic/molecular switches can greatly promote the minimization of the devices and meet the requirement of high integration. This review highlights key developments in the fabrication and application of molecular switching devices. This overview offers valuable insights into the switching mechanisms under various stimuli, emphasizing structural and energy state changes in the core molecules. Beyond the molecular switches, typical individual metal atomic switches are further introduced. A critical discussion of the main challenges for realizing and developing practical molecular/atomic switches is provided. These analyses and summaries will contribute to a comprehensive understanding of the switch mechanisms, providing guidance for the rational design of functional nanoswitch devices toward practical applications.

Application of artificial neural synapses in soft robots

Artificial neural network is considered to be one of the effective ways to enable soft robots to achieve high-performance control due to their significant advantages, such as massively parallel processing and distributed storage of information, adaptivity, and fault tolerance. Artificial neural networks are composed of microelectronic components connected together, of which the most basic units are artificial neural synaptic units, such as atomic switches, memristors, and synaptic transistors. This paper first introduces the research status of soft robots and artificial neural synapses, predicts the demand of soft robots for artificial neural synapses, summarizes the difficulties and problems that may be encountered in the application of artificial neural synapses to soft robots, and finally points out the importance and feasibility of artificial neural synapses in the research and development of soft robots.

Effect of Electrochemically Active Top Electrode Materials on Nanoionic Conductive Bridge Y2O3 Random-Access Memory.

Herein, sol-gel-processed Y2O3 resistive random-access memory (RRAM) devices were fabricated. The top electrodes (TEs), such as Ag or Cu, affect the electrical characteristics of the Y2O3 RRAM devices. The oxidation process, mobile ion migration speed, and reduction process all impact the conductive filament formation of the indium-tin-oxide (ITO)/Y2O3/Ag and ITO/Y2O3/Cu RRAM devices. Between Ag and Cu, Cu can easily be oxidized due to its standard redox potential values. However, the conductive filament is easily formed using Ag TEs. After triggering the oxidation process, the formed Ag mobile metal ions can migrate faster inside Y2O3 active channel materials when compared to the formed Cu mobile metal ions. The fast migration inside the Y2O3 active channel materials successfully reduces the SET voltage and improves the number of programming-erasing cycles, i.e., endurance, which is one of the nonvolatile memory parameters. These results elucidate the importance of the electrochemical properties of TEs, providing a deeper understanding of how these factors influence the resistive switching characteristics of metal oxide-based atomic switches and conductive-metal-bridge-filament-based cells.

Influence of unique behaviors in an atomic switch operation on hardware-based deep learning

Hardware-based deep learning using neuromorphic elements are gathering much attention to substitute the standard von Neuman computational architectures. Atomic switches can be candidate for the operating elements due to their analog resistance change in nonlinear and non-volatile manner. However, there are also several concerns in using atomic switches, such as inaccuracies in resistance control and autonomous weight decay. These characteristics can cause unintentional changes of weights during the learning process. In this study, we simulated how these characteristics of atomic switches influence the accuracy and the power consumption of the deep leaning. By implementing the weight decay, the accuracy remained high despite of the high error level. Power consumption also improved with weight decay in high error level.

Effect of nonlinearity induced by atomic switch in Ag/Ag2S nanoparticles on performance of in-materio reservoir computing

A random network of Ag/Ag2S nanoparticles (NPs) was used as a physical system in reservoir computing (RC) because the network has nonlinear and dynamical characteristics. Ag/Ag2S NPs were synthesized by the modified Brust–Schiffrin method. Atomic switching among the NPs caused nonlinear dynamical behavior of the random network. The Fourier transform of output signals indicated that the generated harmonics were far higher with a larger amplitude of the input sine wave because the atomic switching occurred only at high bias voltages. Higher accuracy was achieved in the Boolean logic RC task because of the nonlinearity originating from switching. These findings suggest that nonlinearity plays a fundamental role in the design and implementation of RC devices.

Simulation of a physical reservoir made of a Ag2S islands network

Recently, a physical reservoir operation utilizing atomic switch technologies was demonstrated. Atomic switch operates by controlling the formation and annihilation of a metal filament between two electrodes using solid-state electrochemical reactions. In this study, we simulated the operation of an atomic switch-based reservoir by arranging modeled atomic switches in a network. The aim of this study is to confirm that nonlinear transformation and short-term memory in a reservoir operation observed in the experiment can be realized by the integration of atomic switches showing nonvolatile bipolar operation. We incorporated these characteristics by making a simple operating model of a single atomic switch, which successfully reproduced major characteristics of the experimental results of a reservoir operation.

Voltage-time dilemma and stochastic threshold-voltage variation in pure-silver atomic switches

Voltage-time dilemma and stochastic threshold-voltage variation in pure-silver atomic switches

Field-mediated neural transmission within an Ag[formula omitted]S gap-type atomic switch

Unlike conventional synaptic transmission, biological field-mediated neural transmissions solely due to extracellular field effects are hardly emphasized in artificial synapse systems. Using an Ag2S gap-type atomic switch, we demonstrate the field-mediated transmission for low bias stimulations which is capable of generating measurable current due to pre-conditioning of the junction by a change in the electrochemical potential of ions prior to any precipitation. The observed current–voltage (I−V) characteristics for V<100 mV are in good agreement with Simmon’s tunneling model for asymmetric barrier (ϕ) in the range 0<V<ϕ. For more than 100 consecutive voltage sweep cycles, an increase in hysteresis corresponding to an input energy of 10−1000 pJ was observed. The average barrier height was lowered from 1 eV to 0.125 eV and the effective mass of carrier electrons increased from 0.25 me to 2.5 me until the actual precipitation began. These results are of practical importance in developing artificial neural networks capable of mimicking field-mediated communications found in the cerebellum, heart, retina, and olfactory systems.

Mel-frequency cepstral coefficients feature extracted voice recognition task using atomic switch Ag/Ag&lt;sub&gt;2&lt;/sub&gt;S device-based time-delayed reservoir computing

Mel-frequency cepstral coefficients feature extracted voice recognition task using atomic switch Ag/Ag&lt;sub&gt;2&lt;/sub&gt;S device-based time-delayed reservoir computing

Liquid-Liquid Interface-Assisted Self-Assembly of Ag-Doped ZnO Nanosheets for Atomic Switch Application.

Developing facile and inexpensive methods for obtaining large-area two-dimensional semiconducting nanosheets is highly desirable for mass-scale device application. Here, we report a method for producing uniform and large-area films of a Ag-doped ZnO (AZO) nanosheet network via self-assembly at the hexane-water interface by controlling the solute/solvent ratio. The self-assembled film comprises of uniformly tiled nanosheets with size ∼1 μm and thicknesses∼60-100 nm. Using these films in a Pt/AZO/Ag structure, an atomic switch operation is realized. The switching mechanism is found to be governed by electrochemical metallization with nucleation as the rate-limiting step. Our results establish the protocol for large-scale device applications of AZO nanosheets for exploring advanced atomic switch-based neuromorphic systems.

Engineering Atomic-Scale Patterning and Resistive Switching in 2D Crystals and Application in Image Processing.

The ultrathin thickness of 2D layered materials affords the control of their properties through defects, surface modification, and electrostatic fields more efficiently compared with bulk architecture. In particular, patterning design, such as moiré superlattice patterns and spatially periodic dielectric structures, are demonstrated to possess the ability to precisely control the local atomic and electronic environment at large scale, thus providing extra degrees of freedom to realize tailored material properties and device functionality. Here,the scalable atomic-scale patterning in superionic cuprous telluride by using the bonding difference at nonequivalent copper sites is reported. Moreover, benefitting from the natural coupling of ordered and disordered sublattices,controllable piezoelectricity-like multilevel switching and bipolar switching with the designed crystal structure and electrical contact is realized, and their application in image enhancement is demonstrated. This work extends the known classes of patternable crystals and atomic switching devices, and ushers in a frontier for image processing with memristors.

Biomembrane‐Based Memcapacitive Reservoir Computing System for Energy‐Efficient Temporal Data Processing

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy‐dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy‐efficient approach. Herein, volatile biomembrane‐based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken‐digit classification and a normalized mean square error of in a second‐order nonlinear regression task. Furthermore, to showcase the device's real‐time temporal data processing capability, a 100% accuracy for an epilepsy detection problem is achieved. Most importantly, it is demonstrated that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms, orders of magnitude lower than those achieved by state‐of‐the‐art devices. Lastly, it is believed that the biocompatible, soft nature of our memcapacitor renders it highly suitable for computing applications in biological environments.

Criticality and Neuromorphic Sensing in a Single Memristor.

Resistive random access memory (RRAM) is an important technology for both data storage and neuromorphic computation, where the dynamics of nanoscale conductive filaments lies at the core of the technology. Here, we analyze the current noise of various silicon-based memristors that involves the creation of a percolation path at the intermediate phase of filament growth. Remarkably, we find that these atomic switching events follow scale-free avalanche dynamics with exponents satisfying the criteria for criticality. We further prove that the switching dynamics are universal and show little dependence on device sizes or material features. Utilizing criticality in memristors, we simulate the functionality of hair cells in auditory sensory systems by observing the frequency selectivity of input stimuli with tunable characteristic frequency. We further demonstrate a single-memristor-based sensing primitive for representation of input stimuli that exceeds the theoretical limits dictated by the Nyquist-Shannon theorem.

Effective threshold voltage modulation technique for steep-slope 2D atomic threshold switching field-effect transistor

Two-dimensional (2D) atomic threshold switching field-effect transistors (ATS-FETs), which integrate 2D FET with threshold switching (TS) devices, have garnered attention as part of the subthreshold swing (SS) improvement for next-generation low-power devices. For industrial applications of 2D ATS-FET, it is important to secure the threshold voltage (Vth) modulation technique. Here, Vth engineering is performed by altering the counter electrode (CE) of a TS device, and the electrical performance of the ATS-FET is systematically investigated. The work function difference between the active electrode and CE alters the internal electric field (E-field) formed between these two electrodes. This severely affects the metal ion migration of the active electrode and induces the Vth shift of the ATS-FET. Because the proposed Vth adjusting technique does not affect the channel material, the MoS2 ATS-FET with the proposed technique can shift Vth while maintaining a high on–off ratio of >105 A on average and achieves an ultra-low average SS of ∼10.929 mV/dec. Moreover, the SS variation due to the random interface traps between the channel and gate dielectric is sufficiently suppressed. This study is expected to be a cornerstone for ATS-FET research by offering a compact platform to adjust Vth without deteriorating steep-slope characteristics.

Performance improvement of a Ag-ion controlled molecular-gap atomic switch by reducing a switching area for applying to a deep learning system

In today’s advanced information society, hardware-based neuromorphic systems attract much attention for achieving more efficient information processing. Hardware-based neuromorphic systems need devices that change their resistance in an analog manner like biological synapses. A molecular-gap atomic switch exhibits analog resistance change over a wider range compared to other non-volatile memory devices. However, several issues remain with the device, such as in cyclic endurance and retention. In this study, we fabricated a molecular-gap atomic switch with a reduced switching area. We expected that the reduction would limit the number of Ag+ cations that contribute to a switching phenomenon and solve the remaining issues. The fabricated devices endured 1000 switching cycles and exhibited stable analog resistance change. Deep learning was successfully demonstrated using 293 fabricated devices as synapses, which resulted in the accuracy of 93.65% in 26th epoch in a 5 × 5 pixel image classification task.

Yield improvement in fabrication of a molecular-gap atomic switch by eliminating potential leakage current paths

A molecular-gap atomic switch is one of the emerging devices that works as a synaptic device. It shows good enough performance such as analog resistance change over five orders of magnitude. However, low yield in device fabrication due to short-circuit of as-fabricated devices has been a big issue. In this study, we Investigated the causes of the low yield and found several possible leakage current paths in unexpected routes. A new device structure and fabrication processes that eliminate the potential leakage paths were proposed. Operating characteristics were evaluated at each step in the improvement, and finally yield in the device fabrication was improved from 10% to 80%.