Functional Emulation Research Articles

High‐Throughput Screening Thickness‐Dependent Resistive Switching in SrTiO3 Thin Films for Robust Electronic Synapse

AbstractThe functionalities and applications of oxide thin films are highly dependent on their thickness. Most thickness‐dependent studies on oxide thin films require the preparation of independent samples, which is labor‐intensive and time‐consuming and inevitably introduces experimental errors. To address this challenge, a general strategy based on high‐throughput pulsed laser deposition technology is proposed to precisely control the thin‐film thickness in local regions under similar growth conditions. The as‐proposed synthesis strategy is demonstrated using typical complex oxide materials of SrTiO3 (STO). Consequently, high‐throughput STO thin films with nine gradient thicknesses ranging from 10.1 to 30.5 nm are fabricated. Notably, a transition from the unipolar to the bipolar resistive switching mode is observed with increasing STO thickness. Moreover, a physical mechanism based on the heterostructure‐mediated redistribution of oxygen vacancies is employed to interpret the transition between the two memristive patterns. The screening of STO thin films with different resistive switching behaviors revealed that the STO thin film with a thickness of 20.3 nm exhibit excellent conductance modulation properties under the application of electrical pulses as well as significant reliability for the emulation of various synaptic functions, rendering it a promising material for artificial neuromorphic computing applications.

Flexible multiterminal photoelectronic neurotransistors based on self‐assembled rubber semiconductors for spatiotemporal information processing

AbstractA significant step toward constructing high‐efficiency neuromorphic systems is the electronic emulation of advanced synaptic functions of the human brain. While previous studies have focused on mimicking the basic functions of synapses using single‐gate transistors, multigate transistors offer an opportunity to simulate more complex and advanced memory‐forming behaviors in biological synapses. In this study, a simple and general method is used to assemble rubber semiconductors into suspended two‐phase composite films that are transferred to the surface of the ion‐conducting membrane to fabricate flexible multiterminal photoelectronic neurotransistors. The suspended ion conductive film is used as the gate dielectrics and supporting substrate. The prepared devices exhibit excellent electrical stability and mechanical flexibility after being bent. Basic photoelectronic synaptic behavior and pulse‐dependent plasticity are emulated. Furthermore, the device realizes the spatiotemporally integrated electrical and optical stimuli to mimic spatiotemporal information processing. This study provides a promising direction for constructing more complex spiking neural networks and more powerful neuromorphic systems with brain‐like dynamic spatiotemporal processing functions.

Functional emulator designs for a memristor model with programmable analog and digital platforms

Hardware validation and verification studies for emulator circuits of the memristor element usually include either analog or digital realization results. In this study, the implementation stages of a memristor element using the Joglekar window function with programmable and reconfigurable analog and digital platforms are given together in order to observe the characteristic current–voltage relationship, experimentally. Herein, a Joglekar window function-based voltage-controlled memristor has been implemented with the field-programmable analog array (FPAA) platform for the first time. In fact, the FPAA platform has attracted attention thanks to its analog nature, rapid prototyping and low hardware cost specialties, and because different nonlinear functions can be easily constructed on this device. Similarly, the field-programmable gate array (FPGA) platform has recently been recognized for its parallel processing feature in digital signal processing studies. The FPGA-based digital implementation stages of the Joglekar window function-based memristor element are also addressed in this study. Thus, the FPAA and FPGA based memristor emulator designs are presented together in order to contribute the memristor-based analog and digital applications that is required to obtain real-time signals.

Highly adaptive and energy efficient neuromorphic computation enabled by deep-spike heterostructure photonic neuro-transistors

Recently, neuromorphic photonics using optical signal as a data domain are considered as a promising solution to realize the next generation neural network platform. Here, metal-chalcogenide/metal oxide semiconductor based photonic neuro-transistors with deep spike-like heterostructure are proposed as a highly adaptive and energy efficient neuromorphic device. In particular, the energy band structure of cadmium sulfide (CdS)/amorphous indium-gallium-zinc-oxide (a-IGZO) heterojunction is engineered via mediating the anion-to-cation ratio of CdS films. It is revealed that the S/Cd ratio is able to determine the work function of the film which consequently causes a variation in the degree of band-bending at the heterointerface. Using a CdS film with optimized S/Cd ratio (CdS1.2), deep spike-like heterostructure (DHS) can be constructed which enables efficient accumulation of photo-generated charge carriers and the emulation of biological synaptic functions including long-term potentiation (LTP) and depression (LTD) behaviors. Also, the a-IGZO/CdS1.2 DHS transistor exhibits low non-linearity value for LTP (1.1) and less energy consumption (45.04 pJ). Furthermore, 7 × 7 opteoelectronic neuromorphic arrays are successfully implemented to exhibit possibility of realization of hardware-based weight pixel training. In addition, the a-IGZO/CdS1.2 DHS transistor shows a high accuracy for image pattern recognition (85.96%) based on the artificial neural network simulation, proving the feasibility in the artificial intelligent systems.

Effect of temperature variations on the behavior of a two-terminal organic–inorganic halide perovskite rewritable memristor for neuromorphic operations

Emulation of biological signal processing, learning and memory functions is essential for the development of artificial learning circuitry. Here, we report on the fabrication of organic–inorganic hybrid perovskite based two-terminal rewritable memristor for neuromorphic operations. We investigated the temperature variations in the current-voltage (I–V) characteristics behavior of a two-terminal organic–inorganic perovskite – graphene oxide rewritable memristor in planar geometry. The resistance switching effect has been found to have a switching time of ∼40 ns and it is more pronounced at temperatures below 250 K, then ionic conductivity freezes and electron transport predominates. This effect is observed at both: positive and negative voltages and the hysteresis of the I-Vs decreases significantly at T < 250 K. The change in conductance at temperatures from 290 K down to 100 K is attributed to the transition from ion migration to electronic conduction under external electric field with possible influence of phase structural transition in perovskite film at T < 200 K.

Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing

Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid electrolyte as the gate dielectric can be routinely operated at gate voltages (VGS) below − 1.5 V, subthreshold-swings (S) smaller than 120 mV/dec, and ON/OFF current ratio larger than 108. Large hysteresis in transfer curves depicts the signature of non-volatile resistive switching (RS) property with ON/OFF ratio as high as 105. In addition, our memT device also shows many synaptic functions, including the availability of many conducting-states (> 500) that are used for efficient pattern recognition using the simplest neural network simulation model with training and test accuracy higher than 90%. Overall, the presented approach opens a new and promising way to fabricate high-performance artificial synapses and their arrays for the implementation of hardware-oriented neural network.

A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing.

Artificial synapses based on electrolyte gated transistors with conductance modulation characteristics have demonstrated their great potential in emulating the memory functions in the human brain for neuromorphic computing. While previous studies are mostly focused on the emulation of the basic memory functions of homo-synapses using single-gate transistors, multi-gate transistors offer opportunities for the mimicry of more complex and advanced memory formation behaviors in biological hetero-synapses. In this work, we demonstrate an artificial hetero-synapse based on a dual-gate electrolyte transistor that can implement in situ spatiotemporal information integration and storage. We show that electric pulses applied on a single gate or unsynchronized electric pulses applied on dual gates only induce volatile conductance modulation for short-term memory emulation. In contrast, the device integrates the electric pulses coincidently applied on the dual gates in a supralinear manner and exhibits nonvolatile conductance modulation, enabling long-term memory emulation. Further studies prove that artificial neural networks based on such hetero-synaptic transistors can autonomously filter the random noise signals in the dual-gate inputs during spatiotemporal integration, facilitating the formation of accurate and stable memory. Compared to the single-gate synaptic transistor, the classification accuracy of MNIST handwritten digits using the hetero-synaptic transistor is improved from 89.3% to 99.0%. These findings demonstrate the great potential of multi-gate hetero-synaptic transistors in simulating complex spatiotemporal information processing functions and provide new platforms for the design of advanced neuromorphic computing systems.

Halide perovskite two-terminal analog memristor capable of photo-activated synaptic weight modulation for neuromorphic computing

Emulation of biological signal processing, learning and memory functions is essential for the development of artificial learning circuitry. Two terminal artificial synapses are supposed to be more feasible with biological system in terms of energy efficiency and processing. Here, we report on the fabrication of organic–inorganic hybrid perovskite based two-terminal artificial synapse in which the synaptic plasticity is modified by both voltage pulses and light illumination. The device emulates important synaptic characteristics, including analog memory switching, short-term plasticity, and long-term plasticity, analogous to the biological system. The change in conductance is attributed to the ion migration under external electric field. In addition, the improved post-synaptic current in optical exposer could be related to the generation of excitons and lowered Schottky barrier at perovskite/electrode interface under external electric field.

Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles.

The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.

Developing a novel functional disc emulator to investigate the nucleus pulposus replacement

We have developed a simple, inexpensive and innovative device for reproducing the global mechanical behavior of spinal motion segments and the local mechanical environment experienced by lumbar intervertebral discs. The device has several broad functions: (1) exploration of the basic mechanics underlying this complex skeletal system, (2) connecting changes in tissue characteristics with overall motion segment function, and (3) evaluation of strategies for repair and replacement of disc components. This “disc emulator” consists of three main parts: (1) an artificial annulus fibrosus (AAF), made out of silicone, with lumbar disc geometry and adjustable material properties, (2) a hydrogel nucleus pulposus (NP) also with lumbar disc geometry and adjustable material properties, and (3) simulated vertebral bodies 3D printed with trabecular bone simulated by a rigid polymer (Acrylonitrile Butadiene Styrene, ABS) and end plates crafted from a compliant polymer (Thermoplastic Polyurethane, TPU). Mechanical compression experiments have been conducted using the disc emulator under similar protocols to published studies of human cadaver samples. Bulging of the artificial annulus fibrosus was examined under axial compression loads using digital image correlation (DIC), and results show close agreement. We see this approach of using anatomical geometry and multiple adjustable components as a useful means of creating accurate local stress/strain environments for preliminary material evaluation, without the variability and difficulty inherent indirect testing of cadaveric materials.

Diffusing the Abolitionist Norm in Japan: EU ‘Death Penalty Diplomacy’ and the Gap between Rhetoric and Reality in EU–Japan Relations

AbstractThis article uses Börzel and Risse's norm diffusion framework to conceptualize and evaluate the EU's ‘death penalty diplomacy’ in Japan. Despite the ‘exceptional’ nature of Japanese politics with regard to the death penalty the EU has enjoyed numerous successes in its attempts to diffuse the abolitionist norm within Japan. These successes have occurred through both direct and indirect methods of norm diffusion, and through socialization, persuasion and functional emulation. Despite the dramatic increase in executions in Japan in 2018, in practice the EU and Japan have established an understanding that executions will in future stabilize at a low and symbolic level. The EU has also co‐funded research that successfully challenged the Japanese government's public opinion polling‐based justification for retaining the death penalty. This research also played an important role in socializing and persuading the Japanese Federation of Bar Associations to change its official position and to support the abolition of the death penalty.

Towards engineering in memristors for emerging memory and neuromorphic computing: A review

Resistive random-access memory (RRAM), also known as memristors, having a very simple device structure with two terminals, fulfill almost all of the fundamental requirements of volatile memory, nonvolatile memory, and neuromorphic characteristics. Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials, such as biological materials, perovskites, 2D materials, and transition metal oxides. In this review, we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms. We then discuss emergent memory technologies using memristors, together with its potential neuromorphic applications, by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices, in areas such as I ON/I OFF ratio, endurance, spike time-dependent plasticity (STDP), and paired-pulse facilitation (PPF), among others. The emulation of essential biological synaptic functions realized in various switching materials, including inorganic metal oxides and new organic materials, as well as diverse device structures such as single-layer and multilayer hetero-structured devices, and crossbar arrays, is analyzed in detail. Finally, we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.

Integrated Framework for Development, Emulation, and Testing of High-Level Converter Control Functions for Distributed Generation Sources

With the increase in power installed from distributed generation sources (DGS), many problems have appeared following the utilisation of novel topologies and structures. Higher-level controllers for the safe operation of power grids with a high penetration of DGS must be developed. This task becomes difficult as the size and complexity of modern power systems have increased. The development and testing of these systems under strict human supervision controlling each step of the testing procedure is no longer viable. This paper presents a comprehensive integrated platform based on an HIL emulator for the development and testing of electric power systems rich in DGS. Essentially, DGS is considered a controllable black-box system. A detailed calculation of the power references for an arbitrary DGS is shown. The paper also describes the utilisation of enhanced Python scripting which was designed to load all network-related data, start the emulation, collect the data, and generate reports. Furthermore, fully developed controller built-in oscilloscope functionality is introduced. The incorporated oscilloscope facilitates non-intrusive DGS controller data acquisition and analysis, reducing the need for external measurements. The proposed framework was verified by a case study in which a grid topology consisting of 26 nodes was emulated on parallel CHIL infrastructure. Four active and twenty-two passive nodes were instantiated and their reaction to a distribution network grid faults was examined. DGS converter control was executed in external controllers outside of the dedicated HIL environment. All DGSs were governed by different control strategies, regarding maximisation of active/reactive power production, minimisation of the active/reactive power oscillations, adjustable positive/negative components ratio, or without any limitations under voltage disturbances. Real-time experiments confirmed the usefulness and flexibility of the proposed framework concept.

Emulation of Multi-Rotor Functionalities in Fixed Wing Airplane for Farm Based Land Surveillance with Controlled Fluid Dispersion System

The World is emerging with Technologies in every discipline, and India plays a vital role in the export of agricultural products. Since it has a huge amount of manifestation, it requires a lot of manpower. The Lack of skilled labour can also cause depletion of crops production rate. So the use of Technologies in agriculture reduces manpower drastically and increases efficiency. The production rate of crops in agriculture is based on various parameters like temperature, humidity, rain, etc., which are natural factors and are not controlled by farmers. The field of agriculture also depends on some factors like pests, disease, fertilizers, etc., which can be controlled by giving proper treatment to crops. Even though the use of pesticides can protect the crop, it may also have harmful effects on human health. So usage of technologies like quadcopter is an efficient process. But as it involves a huge amount of Initial and Operational cost, the quadcopters are replaced with Fixed Wing Drones. This paper depicts about the design and development of Fixed Wing Airplane known as AGRIWING, which combines all the desirable features of a conventional agricultural fixed wing such as mapping of farm, gathering heat signatures, etc., with the added capabilities of the autonomous fluid dispensing system, as in the Drone Spraying System with the advantages of Fixed Wing Airplane including reduced cost, improved flight time, robustness and more.

Neuro-Transistor Based on UV-Treated Charge Trapping in MoTe2 for Artificial Synaptic Features.

The diversity of brain functions depend on the release of neurotransmitters in chemical synapses. The back gated three terminal field effect transistors (FETs) are auspicious candidates for the emulation of biological functions to recognize the proficient neuromorphic computing systems. In order to encourage the hysteresis loops, we treated the bottom side of MoTe2 flake with deep ultraviolet light in ambient conditions. Here, we modulate the short-term and long-term memory effects due to the trapping and de-trapping of electron events in few layers of a MoTe2 transistor. However, MoTe2 FETs are investigated to reveal the time constants of electron trapping/de-trapping while applying the gate-voltage pulses. Our devices exploit the hysteresis effect in the transfer curves of MoTe2 FETs to explore the excitatory/inhibitory post-synaptic currents (EPSC/IPSC), long-term potentiation (LTP), long-term depression (LTD), spike timing/amplitude-dependent plasticity (STDP/SADP), and paired pulse facilitation (PPF). Further, the time constants for potentiation and depression is found to be 0.6 and 0.9 s, respectively which seems plausible for biological synapses. In addition, the change of synaptic weight in MoTe2 conductance is found to be 41% at negative gate pulse and 38% for positive gate pulse, respectively. Our findings can provide an essential role in the advancement of smart neuromorphic electronics.

Synaptic Characteristics of an Ultrathin Hexagonal Boron Nitride (h‐BN) Diffusive Memristor

A nano‐sized two‐terminal memristor exhibiting volatile threshold switching (TS) is a promising candidate for the emulation of biological synaptic functions to realize efficient neuromorphic computing systems. The Ca2+ dynamics play a vital role in generating a temporal response for neural functions by changing the synaptic weight of biological synapses. Herein, a thinnest synaptic device is fabricated demonstrating drift dynamics of Ag+ migration through the exfoliated h‐BN sheets, which emulates neuromorphic computing operations. The TS characteristics with a large ION/OFF up to ≈105 lead to bio‐synaptic applications, including short‐term and long‐term memory. The experimental realization of the synaptic behavior is demonstrated with paired‐pulse facilitation (PPF), spike‐rate‐dependent plasticity (SRDP), and transition from short‐term plasticity (STP) to long‐term plasticity (LTP). The transition from STP to LTP in this synaptic device verifies the Atkinson and Shiffrin psychological model of human brain learning experimentally. The input pulses with different spike‐times are used to replicate the synaptic functionalities. The two‐terminal diffusive memristors constructed with thin sheets of 2D‐flexible h‐BN resistive materials may lead to flexible neuromorphic devices for biological applications.

Design-based learning for a sustainable future: student outcomes resulting from a biomimicry curriculum in an evolution course

BackgroundNational institutes and education initiatives emphasize the need to prepare future biologists to apply discoveries in science towards solving problems that are both social and scientific in nature. Research from socio-scientific, design-based, and problem-based learning demonstrates that contextualized, real-world tasks can improve students’ ability to apply their scientific knowledge in practical ways to navigate social problems. Biomimicry Design is an interdisciplinary field requiring biology and design skills; it informs the creation of sustainable designs through emulation of biological structures and functions that arise as a result of natural selection. Notably, engaging in biomimicry design targets an important biology and engineering learning outcome: understanding of how structure influences function. This study leveraged the practices of biomimicry along with those of design-based learning (DBL) to improve student outcomes in an evolutionary biology undergraduate course. Through DBL, the authors aimed to (1) ignite deeper understanding of how structure determines function in nature (a cross-disciplinary concept) and (2) help students to consider new ways this concept can benefit society (a science process skill).ResultsWe randomly assigned two sections of an upper-division evolutionary biology course to either a biomimicry DBL (DBL group) or species comparison (comparison group) curricular design. Students in the course were exposed to a 1-day lesson, then 1-weeklong case study, and then a final project focused on either biomimicry species-to-human design comparisons (DBL condition) or species-to-species comparisons (comparison condition). To assess the targeted outcomes, we analyzed students’ responses from a pre-post assessment. Students in the biomimicry section were more likely to apply their biological structure–function knowledge to societal benefits when leaving the course. Students in both sections showed comparable gains in structure–function understanding, but there was no change in the number of students who used misconception language in their post-course compared to pre-course responses.ConclusionsWe conclude that our DBL curriculum, above and beyond the comparison curriculum, may support students’ ability to apply biological concepts to societal benefits without compromising structure–function understanding. Overall, these results provide rationale for incorporating tasks situated in DBL to address socio-scientific issues in biology courses.

Artificial Neural Networks Based on Memristive Devices: From Device to System

Memristive devices are essential for artificial neural networks (ANNs) due to their similarity to biological synapses and neurons in structure, dynamics, and electrical behaviors. By building a crossbar array, memristive devices can be used to conduct in‐memory computing efficiently. Herein, approaches to realize memristive neural networks (memNNs) from the device level to the system level are introduced with state‐of‐art experimental demonstrations. First, algorithm fundamentals for networks and device fundamentals for synapses and neurons are briefly given to provide guidance for developing ANNs based on memristive devices; second, recent advances in memristive synapses are discussed on the device level, including the optimization of device, the emulation of biological functions and the array integration; third, artificial neurons based on complement metal‐oxide‐semiconductor (CMOS) transistors and memristive devices are described; then, systemic demonstrations and latest developments of memNNs are elaborated; finally, summary and perspective on memristive devices and memNNs are presented.

Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses

An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.

A self-powered artificial retina perception system for image preprocessing based on photovoltaic devices and memristive arrays

Artificial retina perception system is significant to pattern recognition and visual function emulation. However, the recent artificial retina system is mainly reported on the integration of three-terminal transistors, whose structural limitations may result in low processing speeds and high energy consumption due to a low array density and complex line design. Furthermore, the external power source is required to drive devices so that the power consumption of the system would increase. Here we present a self-powered artificial retina perception system by utilizing two-terminal solar cells as artificial neurons and perovskite-based memristors as artificial synapses, ensuring the bio-inspired retina system with extendable crossbar array structure for high-density and low power consumption neural networks. By a light stimulation with various wavelengths and intensities, the electrical pre-synaptic signal is generated in the solar cell and subsequently transferred to the perovskite-based memristor to perform further information preprocessing. Especially, the applicability of the artificial retina system to neuromorphic computing is demonstrated for contrast enhancement and noise reduction. The retina perception system is capable of feature extraction by to implement partial functions of convolutional neural networks (CNNs) on the hardware level with improved recognition rate, boosted recognition speed, and reduced energy consumption. A self-powered artificial retina perception system is proposed by integrating two-terminal solar cells and perovskite-based memristors. The STP-LTP functionality of perovskite-based memristors is achieved via the passivation. This self-driven artificial optic-neural system can execute both the emulation of biological synapses and partial functions (eg. the feature extraction for external signals) of convolutional neural networks. • We design a self-powered artificial retina perception system composed of solar cells and perovskite-based memristors. • Synaptic plasticity can be implemented by solar cells due to the low operating voltage of ~ 0.4 V. • The system has capabilities of image preprocessing, such as contrast enhancement and noise reduction. • High recognition rate of ~ 86% can be implemented within 180 training epochs.