Analog Switch Research Articles

Learning‐Effective Mixed‐Dimensional Halide Perovskite QD Synaptic Array for Self‐Rectifying and Luminous Artificial Neural Networks

AbstractA mixed‐dimensional heterostructure comprising nanomaterials with varying dimensions provides a promising structure for an artificial synapse for reconfigurable neuromorphic functions. In this study, an 8 × 8 memristor crossbar array based on a mixed‐dimensional heterostructure comprising Cs1−xFAxPbBr3 (0.00 ≤ x ≤ 0.15) quantum dots (QDs) and different dimensional interfacial nanomaterial layers between the Al and ITO electrodes is designed and fabricated. This array device exhibits a high yield and reliable self‐rectifying analog switching characteristics with low synaptic‐coupling (SC, up to 5.19 × 10−5) and light emission, facilitating stimuli response visualization and preventing undesired pathways in the network array. Furthermore, because the formamidinium (FA) concentration alters the QD size, thereby engineering interfacial band alignment in the heterostructure, the essential synaptic properties such as dynamic range, SC, and nonlinearity can be improved. Especially, as x increases from 0 to 0.11, the recognition accuracy for the MNIST patterns increases significantly, from 68.97% to 89.08%, even for single‐layer ANNs. The energy consumption required for a specific accuracy level is reduced by a factor of 25.15. The utilization of mixed‐dimensional perovskite QD‐based heterostructures in neural networks may provide desirable neuromorphic electronic functions with enhanced learning capability and energy efficiency, while preventing unwanted neural signals.

Defective Engineering Tuning the Analog Switching Linearity and Symmetry of Two‐Terminal Artificial Synapse for Neuromorphic Systems

AbstractNeuromorphic computing inspired by memristors has gained considerable attention due to its low power and easy integration. However, state‐of‐the‐art two‐terminal resistive switching memristors based on conductive filament formation suffer from high variability and poor controllability. As a three‐terminal device operated through electrochemistry and dynamic insertion/extraction of ions, the electrochemical ion synapse demonstrates deterministic control of electron conductivity based on ion doping. But, integrating the electrochemical ion synapse into crossbar arrays will pose higher challenges and lower integration density. Herein, inspired by first‐principles calculations, a two‐terminal bidirectional plasticity electrochemical artificial synapse with integrated lithium polymer electrolyte and polycrystalline tungsten oxide layer is reported. The linearity and stability of the device weight update are greatly improved by adjusting the defect concentration of the polycrystalline WO3 layer. Even after 16 000 write‐read events in the air, its performance remained almost unchanged. Moreover, it has an over‐limit protection mechanism under one‐way stimulation that exceeds the normal range. Based on this excellent stability, the authors designed and successfully simulated the “muscle memory” that the programmatical organization of the nervous system leads to proficiency in specific actions.

Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning

Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability. Furthermore, the overall power consumption of the system inversely correlates with the RRAM devices conductance. To study material dependence of these properties, TaOx and HfOx RRAM devices in one-transistor one-RRAM configuration (1T1R) were fabricated using a custom 65 nm CMOS fabrication process. Analog switching performance was studied with a range of initial forming compliance current (200–500 µA) and analog switching tests with ultra-short pulse width (300 ps) was carried out. We report that by utilizing low current during electroforming and high compliance current during analog switching, a large number of RRAM conductance states can be achieved while maintaining low conductance state. While both TaOx and HfOx could be switched to more than 20 distinct states, TaOx devices exhibited 10× lower conductance, which reduces total power consumption for array-level operations. Furthermore, we adopted an analog, fully in-memory training algorithm for system-level training accuracy benchmarking and showed that implementing TaOx 1T1R cells could yield an accuracy of up to 96.4% compared to 97% for the floating-point arithmetic baseline, while implementing HfOx devices would yield a maximum accuracy of 90.5%. Our experimental work and benchmarking approach paves the path for future materials engineering in analog-AI hardware for a low-power environment training.

A review on device requirements of resistive random access memory (RRAM)-based neuromorphic computing

With the arrival of the era of big data, the conventional von Neumann architecture is now insufficient owing to its high latency and energy consumption that originate from its separated computing and memory units. Neuromorphic computing, which imitates biological neurons and processes data through parallel procedures between artificial neurons, is now regarded as a promising solution to address these restrictions. Therefore, a device with analog switching for weight update is required to implement neuromorphic computing. Resistive random access memory (RRAM) devices are one of the most promising candidates owing to their fast-switching speed and scalability. RRAM is a non-volatile memory device and operates via resistance changes in its insulating layer. Many RRAM devices exhibiting exceptional performance have been reported. However, these devices only excel in one property. Devices that exhibit excellent performance in all aspects have been rarely proposed. In this Research Update, we summarize five requirements for RRAM devices and discuss the enhancement methods for each aspect. Finally, we suggest directions for the advancement of neuromorphic electronics.

Sequence learning in a spiking neuronal network with memristive synapses

Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience, but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that may efficiently run this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural network simulator NEST. We investigate two types of ReRAM memristive devices: (i) a gradual, analog switching device, and (ii) an abrupt, binary switching device. We study the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate that, in contrast to many other artificial neural networks, this architecture is resilient with respect to changes in the on-off ratio and the conductance resolution, device variability, and device failure.

A Physical Description of the Variability in Single‐ReRAM Devices and Hardware‐Based Neuronal Networks

Resistive switching devices are candidates to be used as weight for the future hardware realization of neuronal networks. Herein, these resistive switches base on oxygen vacancy migration and can be toggled between different resistance states. Internal processes lead to a variability over time in these states. To understand this, a dynamical model is developed, which links the internal physical mechanism to the current response. Thereby, the electronic current is calculated by tunneling processes over oxygen vacancies. The model allows to understand the physical transport properties and the current–voltage dependence. Integrating ionic hopping processes clarifies the variability in the electronic conduction. The derived model does not require empirical input parameters and only depends on the properties of the materials. This allows to use literature values and independently compare them to electrical measurements. The model predicts the experiments correctly. Thereupon, the physical properties of the conduction are investigated with respect to the transport over discrete energy levels, which change under the hopping of oxygen vacancies. Afterwards, the current level and variability are simulated for increasing oxygen vacancy concentrations to investigate analog switching, which is required for neuronal network layers. Finally, the variability is tested with multiple interacting cells in a neuronal layer.

STRATEGI PROGRAMMING INEWS DALAM MEMPERTAHANKAN PROGRAM SIARAN BERITA MENJELANG ASO 2022

This research concentrates on the realm of television studies as an institution, to find out how the strategy of the iNews Siang news program is in maintaining news broadcast programs ahead of ASO 2022. The type of research used is descriptive qualitative research. The object of this research study is the iNEWS Afternoon program ahead of the Migration policy of Analog TV broadcasts to Digital TV broadcasts or Analog Switch Off (ASO). The problems in this study are dissected by analyzing how the strategy of the iNews Siang news program is in maintaining the news broadcast program ahead of ASO 2022, data collection techniques are applied to support information by conducting observations, interviews, and documents. The purpose of this research is to find out the strategy for the INEWS Siang news program in maintaining the news broadcast program ahead of ASO 2022, through the pre-production, production and postproduction stages observed as one unit in a series of program strategies. Participatory observation methods, interviews and literature on the news department, Morrisan's program strategy theory were built as a framework for the approach. The results obtained are iNews TV station programming strategy, especially in the iNews Afternoon Towards ASO 2022 program in broadcasting television broadcast programs to digital so that they can reach all audiences throughout Indonesia. Therefore, there are programs broadcast by the iNews Siang program in preparation for or ahead of ASO 2022. ASO stands for Analog Switch Off. The policy of discontinuing Analog TV broadcasts to be subsequently transferred to digital TV broadcasts is known as Analog Switch Off (ASO).
 Keywords: ASO, iNEWSTV, digital broadcast, programming strategy, television journalist

Bio-inspired artificial heterosynapse based on carbon nanotubes memtransistor with dynamically tunable analog switching behavior

The swift advancement of artificial intelligence necessitates the exigency for a proficient information processing system. Taking inspiration from the intricate synapses of the human brain, we have crafted an artificial heterosynapse utilizing carbon nanotube (CNT) memtransistors. The multi-terminal heterosynaptic plasticity of CNTs memtransistor could be effectively modulated by gate voltage or drain voltage, which resembles the biological synapses affected by external neuromodulators. The artificial synapse skillfully replicates neuromorphic functionalities in response to pulse stimuli. This enables tunable analog switching behavior, multiple memory states, and complex neuromorphic learning. Furthermore, the Pavlovian classical conditioning experiments were conducted utilizing the synaptic device to achieve associative learning. The introduction of the CNTs memtransistor will propel the advancement of neuromorphic device technology and unlock novel opportunities for designing learning schemes with an additional degree of freedom.

Spike-time dependent plasticity of tailored ZnO nanorod-based resistive memory for synaptic learning

Metal oxide resistive memory is a potential device that can substantially influence the current roadmap for nonvolatile memory and neuromorphic computing. However, common amorphous oxide-based resistive random-access memory suffers from high forming voltages that complicate circuit design and abrupt SET behavior incompatible with analog weight updates. To overcome such limitations, wurtzite ZnO nanorods were synthesized on a fluorine-doped tin oxide (FTO) substrate and a bipolar resistive memory with the Ag/w-ZnO/FTO stacking sequence was fabricated. The hexagonal NR morphology of w-ZnO with controlled vertical growth and nanochannel formation between the NRs were produced by in situ crystalline growth. This morphology enabled a forming-free switching and an analog switching effect that emulated neuromorphic functionalities such as potentiation–depression and complex spike-time dependent plasticity-based Hebbian learning rules. Importantly, the device exhibited nonabrupt switching behavior suitable for analog weight updates in neuromorphic computing in contrast to conventional resistive memory.

Vibration Measurements by Self-Mixing Interferometry: An Overview of Configurations and Benchmark Performances

Self-mixing interferometry (SMI) is suitable to sense and measure vibrations of amplitudes ranging from picometers to millimeters at frequencies from sub-Hz to MHz’s. As an optical probe, SMI has the advantage of being non-invasive with the ability to measure without any treatment of the target surface and operate from a substantial standoff distance from the target. As an additional advantage, the SMI configuration is much simpler than that of conventional interferometers as it does not require any optical part external to the laser source. After a short introduction to the basics of SMI, we review the development of configurations of SMI instruments for vibration measurements, based on both analog and digital processing, with record performance to cover the range of vibration amplitudes from 0.1 nm to 1 mm, frequencies up to MHz, and stand-off distances up to 100 m. These performances set a benchmark that is unequaled by other approaches reported so far in the literature. The configurations we describe are (i) a simple MEMS-response testing instrument based on fringe counting, (ii) a half-fringe locking vibrometer for mechanical mode analysis and transfer function measurements, with a wide linear response on six decades of amplitude, (iii) a vibrometer with analog switching cancellation for μm-to-mm amplitude of vibrations, and (iv) a long standoff distance vibrometer for testing large structures at distances up to 100 m and with nm sensitivity. Lastly, as the vibrometer will almost invariably operate on untreated, diffusing surfaces, we provide an evaluation of phase-induced speckle pattern errors affecting the SMI measurement.

Communication strategy in socialising analogue switch off in North Sulawesi

This research delves into the efficacy of the North Sulawesi Broadcasting Commission's communication strategy in facilitating the analogue switch-off programme. The study employs qualitative research methods, utilising in-depth interviews with key stakeholders, and adopts Hafied Cangara's communication strategy framework, based on Laswell's theory , to analyse the Commission's approach. The outcomes reveal that the Commission's communication plan effectively conveyed the intended message to the public. It achieved its objectives of disseminating information and educating the populace about the switch-off process. Nonetheless, the plan could have been further enhanced to better address the diverse needs and concerns of the audience. The findings offer valuable insights into the challenges and opportunities associated with implementing the analogue switch-off programme, especially concerning formulating efficient communication strategies that can effectively reach all segments of Society. By shedding light on these aspects, this research significantly contributes to the broader discourse on digital television and its profound influence on broadcasting policies in Indonesia.

In‐Depth Physical Mechanism Analysis of Polymer Artificial Optoelectronic Synapse with High Endurance and Applications of Visual System and Operant Conditioning

AbstractBeing capable of dealing with both electrical signals and light, artificial optoelectronic synapses are considered to be an important cornerstone of neuromorphic computing. Here, an artificial optoelectronic synapse is reported through a simple solution process using organic poly(3‐hexylthiophene) (P3HT) and a remarkable analog switching characteristic similar to synaptic behavior is observed. The endurance and data retention capability of the P3HT‐based optoelectronic synapse exhibit stable characteristics up to 5000 consecutive cycles and 104 s. Through in‐depth physical mechanism analysis, it is confirmed that the analog switching characteristics of the device are mainly caused by a tunneling mechanism and space charge limited conduction. Furthermore, characterizations such as X‐ray photoelectron spectroscopy and atomic layer deposition prove that the memristive properties of device can be attributed to ion migration. More importantly, the device can co‐modulate the optoelectronic signal and successfully implement a photo‐triggered multi‐signal mode response. Based on this, a 3 × 3 synapse array is developed to demonstrate the potential application of the proposed P3HT‐based optoelectronic synapse in constructing an artificial visual system. Finally, operant conditioning is successfully simulated in the synaptic device. This work provides a reference for the construction of optoelectronic synapses in the neuromorphic visual system.

Analisis Sentimen Pengguna Youtube Mengenai Analog Switch Off Menggunakan Word Embedding Dan Metode Long Short-Term

Analog Switch Off (ASO) or migration program from analog television to digital television is a program issued by the Ministry of Communication and Informatics in Indonesia. Some people provide different responses and opinions on YouTube comments about ASO. There are those who give positive or neutral comments. However, there were also those who gave negative comments. Sentiment analysis is a process that is carried out automatically in studying, retrieving, and processing textual data to obtain information and see responses or opinions about an issue or object towards positive, neutral or negative opinions. Thus sentiment analysis can be used as a reference in making organizational decisions, improving a service, or as a review of a product. Sentiment analysis was performed using word embedding with Word2Vec, and sentiment classification using the Long Short-Term Memory (LSTM) method. The results of the evaluation test are 92% accuracy, 92% precision, 92% recall, and 92% f1-score.

Manipulation of perpendicular magnetic anisotropy and spin–orbit torque switching behavior in ferrimagnetic D022-Mn3Ga based multilayers

Perpendicularly magnetized ferrimagnets have shown potential application in nonvolatile, high-density, and ultrafast spintronic devices. Achieving this requires tuning the material performance of ultrathin ferrimagnetic films. Here, we present an experimental demonstration of tunable perpendicular magnetic anisotropy (PMA) and spin–orbit torque (SOT) switching behavior in a ferrimagnetic Heusler alloy D022-Mn3Ga based multilayers by introducing lattice-matching underlayers. It is found that the highly (001) orientated crystalline structure is well maintained, whereas coercivity, anisotropy field, and PMA constant rely heavily on the specific underlayer. Owing to the strong out-of-plane exchange coupling, the PMA constant of Co2MnSi/D022-Mn3Ga is significantly enhanced by four times and reaches 12.1 Merg/cm3. Moreover, the SOT measurements suggest that Fe/D022-Mn3Ga and Co2MnSi/D022-Mn3Ga exhibit analog and abrupt switching dynamics, respectively. This contrasting SOT switching behavior is attributed to different magnetization switching mechanisms, verified by the direct imaging of the nanoscale magnetic domain. These results provide a deep insight into the manipulation of not only fundamental magnetic properties but also SOT switching of ferrimagnetic Heusler alloys.

Deep reservoir computing based on self-rectifying memristor synapse for time series prediction

Herein, a self-rectifying resistive switching memristor synapse with a Ta/NbOx/Pt structure was demonstrated for deep reservoir computing (RC). The memristor demonstrated stable nonlinear analog switching characteristics, with a rectification ratio of up to 1.6 × 105, good endurance, and high uniformity. Additionally, the memristor exhibited typical short-term plasticity and dynamic synaptic characteristics. Based on these characteristics, a deep memristor RC system was proposed for time series prediction. The system achieved a low normalized root mean square error (NRMSE) of 0.04 in the time series prediction of the Henon map. Even at 90 °C, deep RC retains good predictive power with an NRMSE of only 0.07. This work provides guidance for efficient deep memristive RC networks to handle more complex future temporal tasks.

Analog Ion‐Slicing LiNbO3 Memristor Based on Hopping Transport for Neuromorphic Computing

Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic computing systems has attracted tremendous interest. However, existing analog memristors are still far from accurate tuning of multiple conductance states, which are crucial from the device‐level view. Herein, a reliable analog memristor based on ion‐slicing single‐crystalline LiNbO3 (LNO) thin film is demonstrated. The highly ordered LNO crystal structure provides a stable pathway of oxygen vacancy migration, which is contributed to a stable Mott variable‐range hopping process in trap sites. Excellent analog switching characteristics with high reliability and repeatability, including long retention/great endurance with small fluctuation (fluctuated within 0.22%), a large dynamic range of two orders of magnitude, hundreds of distinguishable conductance states with tunable linearity, and ultralow cyclic variances for multiple weight updating (down to 0.75%), are realized with the proposed memristor. As a result, a multilayer perceptron with a high recognition accuracy of 95.6% for Modified National Institute of Standards and Technology dataset is realized. The proposed analog memristive devices based on ion‐slicing single‐crystalline thin films offer a novel strategy for fabricating high‐performance memristors that combined linear tunability and long‐term repeatability, opening a novel avenue for neuromorphic computing application.

In-Grain Ferroelectric Switching in Sub-5 nm Thin Al0.74 Sc0.26 NFilms at 1 V.

Analog switching in ferroelectric devices promises neuromorphic computing with the highest energy efficiency if limited device scalability can be overcome. To contribute to a solution, one reports on the ferroelectric switching characteristics of sub-5 nm thin Al0.74 Sc0.26 Nfilms grown on Pt/Ti/SiO2 /Siand epitaxial Pt/GaN/sapphiretemplates by sputter-deposition. In this context, the study focuses on the following major achievements compared to previously available wurtzite-type ferroelectrics: 1) Record low switching voltages down to 1V are achieved, which is in a range that can be supplied by standard on-chip voltage sources. 2) Compared to the previously investigated deposition of ultrathin Al1-x Scx Nfilms on epitaxial templates, a significantly larger coercive field (Ec ) to breakdown field ratio is observed for Al0.74 Sc0.26 Nfilms grown on silicon substrates, the technologically most relevant substrate-type. 3) The formation of true ferroelectric domains in wurtzite-type materials is for the first time demonstrated on the atomic scale by scanning transmission electron microscopy (STEM) investigations of a sub-5 nm thin partially switched film. The direct observation of inversion domain boundaries (IDB) within single nm-sized grains supports the theory of a gradual domain-wall driven switching process in wurtzite-type ferroelectrics. Ultimately, this should enable the analog switching necessary for mimicking neuromorphic concepts also in highly scaleddevices.

Analysis of Digital Television User Acceptance After Analog Switch-Off (ASO) Implementation in Jabodetabek

The government officially stopped broadcasting analog television or what is known as Analog Switch Off (ASO) on November 2, 2022, in Jabodetabek. It impacts analog TV users in Jabodetabek, who can no longer receive television broadcasts on their analog televisions. This research aimed to determine user acceptance of digital television broadcasts after implementing ASO in the Jabodetabek area. In the present study, the experience of digital TV users is evaluated using the UTAUT (Unified Theory of Acceptance and Use of Technology) 2 model and the Structural Equation Model (SEM). The data was obtained from a questionnaire distributed through digital media for the Jabodetabek area. According to data processing, the two factors that significantly influence the user experience of digital television broadcasts are the Analog Switch Off (ASO) policy factor and the habit factor. The hypothesized model suitability test fits the resulting data and can affect the Behavioral Intention 41.4.

Accurate Sneak-Path-Controlled Readout for a Cross-Point Resistive Sensor Array

A readout method of measuring resistances from a sensor array is proposed. As opposed to the conventional method of feedback readout, in which the crosstalk is suppressed by an op-amp, the sneak paths are controlled by switches, and a matrix equation of the circuit is derived from measured voltage. Thus, the scanning speed and power consumption of an interface circuit are not inherently deteriorated by the op-amp. The main factors affecting readout accuracy are parasitic resistance of the analog switch and quantized error of the analog-to-digital converters (ADCs). Therefore, calibration of switch resistance is also proposed to maintain readout accuracy by additional ADCs. To evaluate the proposed readout system, “sneak-path-controlled readout” (SPCR) and “accurate sneak-path-controlled readout” (ASPCR) are implemented. To verify the accuracy of the proposed readout method, the interface circuit and the calibration of switch resistance were simulated by MATLAB. The target array size was 32×32, and the sensor resistances were distributed in the same manner as real sensors. Since readout performance is also affected by the pattern of resistances, several patterns of resistance were prepared, and their results were compared. The results of the simulations show that SPCR using a 16-bit ADC can read normally distributed sensors with readout error of 0.05%. Moreover, ASPCR can handle a wider resistance distribution, and its readout error was less than 0.3% with a resistance pattern in which resistance ranges from 1.71 kΩ to 217 kΩ.

Hybrid Tuning of Sub‐Filaments to Improve Analog Switching Performance in Memristive Devices

AbstractMemristive devices are promising candidates for analog computing applications such as neuromorphic computation. Larger dynamic ranges and more sufficient multilevel states can enable the significant development of memristor‐based utilizations. Herein, a method to improve the analog switching performance of memristors through a hybrid tuning (coarse and fine tuning) of two sub‐filaments is demonstrated. The creation of sub‐filaments inside the dielectric switching layer is realized by deploying Pt metal islands in the switching layer. Given the different material stack configurations of the two sub‐filaments, they exhibit different switching properties to play the roles of coarse and fine tuning respectively in the memristor. Based on the above mechanism, a Pt/Ta/Al2O3/Pt island/Al2O3‐x/TiOy/Al2O3‐x/Pt memristor is proposed and fabricated. Through the hybrid tuning of two sub‐filaments, a combined dynamic range of 600 Ω to 50 kΩ is achieved. Compared to the reference Pt/Ta/Al2O3/Pt memristors (dynamic range: 600 Ω to 8 kΩ), both dynamic range and multilevel resistance states are increased significantly. Meanwhile, the energy efficiency is improved because the resistance of tunable states can be set to larger values. Furthermore, this mechanism can be incorporated into various existing memristors to improve their dynamic range and multilevel states, which extensively enriches the applications of memristors.