Multilevel States Research Articles

Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.

Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼103 s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.

A new nonlinear fatigue cumulative damage model based on load interaction and strength degradation

A new nonlinear fatigue cumulative damage model is proposed to address the challenge of insufficient accuracy in calculations stemming from nonlinear cumulative damage models that fail to account for strength degradation effects and interactions among multi-level loads. This model, an enhancement of the Aeran fatigue damage model, incorporates stress ratios to capture load interactions and includes a logarithmic residual strength degradation model extended to multi-level stress states. Comparative analysis of this model against the Miner model and two other models across various material fatigue datasets shows superior predictive accuracy. Specifically, the new model demonstrates a 74.43% improvement over the Aeran model under six-level loading conditions. Its straightforward mathematical formulation makes it practical for engineering applications in fatigue life prediction.

Controllable multilevel quantized conduction states in vertical CBRAM using Cu-nanodot ion source

Restricting the injection of cations is crucial for implementing precise quantized conduction (QC) during the multi-level operation of conductive-bridge random-access memory (CBRAM). This study proposes a method that controls ion supply by confining the Cu ion source to 0D in a vertical structure. This confinement enables sophisticated filament control for multilevel operation. Cu nanodots are formed between the W electrodes, with W and Cu serving as the electron and ion sources for the conducting filament, respectively. When the Cu filament is confined to 0D, the controllability of the QC implementation is better than that in cases where the filament is restricted to 1D or bulk Cu. The highest number of quantized levels was observed for 0D Cu, which can be attributed to the synergistic effects of filament confinement and the decrease in the Cu electrochemical reaction rate. Furthermore, we analyzed the switching mechanism of the Cu nanodots by employing the activation energy extracted by the slope of the voltage-time dilemma. Our results demonstrate the effectiveness of confining the ion source to 0D for achieving precise filament control in CBRAM and enabling its applicability to vertical structures.

The Fabrication of Polyimide-Based Tunable Charge Traps Ternary Memristors Doped with Ni-Co Coated Carbon Composite Nanofibers.

In the dynamic fields of information science and electronic technology, there is a notable trend towards leveraging carbon materials, favored for their ease of synthesis, biocompatibility, and abundance. This trend is particularly evident in the development of memristors, benefiting from the unique electronic properties of carbon to enhance device performance. This study utilizes sensitized chemical evaporation and spin-coating carbonization techniques to fabricate nickel-cobalt coated carbon composite nanofibers (SC-NCMNTs). Novel polyimide (PI) matrix composite memory devices were fabricated using in situ polymerization technology. Transmission electron microscopy (TEM) and micro-Raman spectroscopy analyses validated the presence of dual interface structures located between the Ni-Co-MWNTs, carbon composite nanofibers, and PI matrix, revealing a significant number of defects within the SC-NCMNTs/PI composite films. Consequently, this results in a tunable charge trap-based ternary resistive switching behavior of the composite memory devices, exhibiting a high ON/OFF current ratio of 104 and a retention time of 2500 s at an operating voltage of less than 3 V. The mechanism of resistive switching is thoroughly elucidated through a comprehensive charge transport model, incorporating molecular orbital energy levels. This study provides valuable insights for the rational design and fabrication of efficient memristors characterized by multilevel resistive switching states.

Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing.

The exotic properties of three-dimensional halide perovskites, such as mixed ionic-electronic conductivity and feasible ion migration, have enabled them to challenge traditional memristive materials. However, the poor moisture stability and difficulty in controlling ion transport due to their polycrystalline nature have hindered their use as a neuromorphic hardware. Recently, two-dimensional (2D) halide perovskites have emerged as promising artificial synapses owing to their phase versatility, microstructural anisotropy in electrical and optoelectronic properties, and excellent moisture resistance. However, their asymmetrical and nonlinear conductance changes still limit the efficiency of training and accuracy of inference. Here we achieve highly linear and symmetrical conductance changes in Dion-Jacobson 2D perovskites. We further build a 7 × 7 crossbar array based on analogue perovskite synapses, achieving a high device yield, low variation with synaptic weight storing capability, multi-level analogue states with long retention, and moisture stability over 7 months. We explore the potential of such devices in large-scale image inference via simulations and show an accuracy within 0.08% of the theoretical limit. The excellent device performance is attributed to the elimination of gaps between inorganic layers, allowing the halide vacancies to migrate homogeneously regardless of grain boundaries. This was confirmed by first-principles calculations and experimental analysis.

A multilevel resistive switching memristor based on flexible organic–inorganic hybrid film with recognition function

Abstract Brain-inspired neuromorphic computing systems fueled the emergence of memristor-based artificial synapses, however, conventional silicon-based devices restricted their usage in the wearable field because of their difficulty in bending. To tackle the above challenge, a vertically structured flexible memristor with aluminum-based hydroquinone organic–inorganic hybrid film and Al2O3 as the functional layer, ITO and Pt as the bottom and top electrodes, and PET as the substrate has been developed utilizing molecular/atomic layer deposition to achieve a tradeoff between the resistive transition properties and the flexibility of memristors. The obtained devices combine stable resistive switching behavior and flexibility, showing high switching ratio of 103, better retention (up to 105 s) and endurance properties (up to 104 cycles), and robustness at radius of curvature of 4.5 mm after 104 bending cycles. Furthermore, the presence of multilevel resistive states in these devices ensures that the memristor can emulate synaptic properties such as paired-pulse facilitation, transition from short-term plasticity to long-term plasticity, long-term potentiation and depression, and spike-time-dependent plasticity. The resistive switching mechanism and the role of the bending state on the electrical performance of the device are explored. The fully connected artificial neural network based on the memristor can achieve a recognition accuracy of 90.2% for handwritten digits after training and learning. Flexible memristor will bring feasible advances to the integration of neuromorphic computing and wearable functionality.

Subjective Perceptions of Difference in Multi-level States: Regional Values, Embeddedness, and Bias in Canadian Provinces

Subjective Perceptions of Difference in Multi-level States: Regional Values, Embeddedness, and Bias in Canadian Provinces

LiNbO3-based ferroelectric tunnel junctions with changeable electroresistance for data storage

Ferroelectric tunnel junctions (FTJs) have attracted considerable attention for potential applications in the next-generation data storage technologies. In this work, we have grown high-quality LiNbO3 single crystal films on STO (111) substrates by rotational epitaxy. The certain ferroelectricity was achieved in nanoscale epitaxial LiNbO3 films. Then, the Au/LiNbO3/Nb: SrTiO3 FTJ was fabricated, and the nonvolatile resistive switching controlled by the nonvolatile polarization switching was observed. The Au/LiNbO3/Nb: SrTiO3 FTJs regulate the quantum tunneling effect through ferroelectric polarization reversal to obtain multi-level resistive states, thereby achieving data storage functionality. At room temperature, the ON/OFF current ratio can exceed 103. Furthermore, the FTJs also exhibit excellent retention for more than 103 s and good switching endurance for 2000 cycles. The results suggest the application potential of this LiNbO3-based FTJ for next generation nonvolatile ferroelectric memories.

Passive LiNbO₃ Memristor With Multilevel States for Neuromorphic Computing

Passive LiNbO₃ Memristor With Multilevel States for Neuromorphic Computing

Reconfigurable Multilevel Storage and Neuromorphic Computing Based on Multilayer Phase-Change Memory.

In the era of big data, the amount of global data is increasing exponentially, and the storage and processing of massive data put forward higher requirements for memory. To deal with this challenge, high-density memory and neuromorphic computing have been widely investigated. Here, a gradient-doped multilayer phase-change memory with two-level states, four-level states, and linear conductance evolution using different pulse operations is proposed. The mechanism of multilevel states is revealed through high-resolution transmission electron microscopy (HRTEM) and finite-element analysis (FEA), which show that the sequential phase change among different sublayers is realized due to the different physical properties of the sublayers with different doping concentrations. Taking advantage of the devices' linear conductance evolution characteristic, a handwritten digit (28 × 28 pixel) recognition task is implemented with a high learning accuracy of 93.46% by building a simulated artificial neural network made up of this gradient-doped multilayer phase-change memory. It is proved that this gradient-doped multilayer phase-change memory is capable of both binary multilevel digital storage and brain-inspired analog in-memory computing in the same device, enabling reconfigurable applications in the future.

Convolutional neural network for high-performance reservoir computing using dynamic memristors

In the rapidly advancing field of neuromorphic computing, W/ZnO/TiN resistive random-access memory (RRAM) devices have emerged as a next-generation computational building block. Our findings reveal the significant role played by the thickness of the ZnO layer in determining the electrical properties essential for data storage and neuromorphic applications. The short-term memory (STM) capabilities, which are critical for processing temporal information, are closely examined alongside their potential to simulate biological synaptic functions through multilevel conductance states and synaptic behaviors such as paired-pulse facilitation. Integrating these devices into reservoir computing systems enhances pattern recognition and accelerates learning, which demonstrates their utility in sequential data processing. In addition, conductance modulation via pulse width adjustment is a novel strategy to optimize memory device performance. By showcasing the effectiveness of W/ZnO/TiN devices in neuromorphic computing through high-accuracy image recognition tasks, our study highlights their foundational role in advancing neuromorphic computing technologies. The adaptability, learning capabilities, and efficiency of these devices underscore their potential for developing hardware-based neuromorphic systems that are capable of complex data processing.

Vanadium Dioxide by Atomic Layer Deposition: A Promising Material for Next-Generation Memory Devices.

The synthesis of a vanadium dioxide (VO2) film using atomic layer deposition (ALD) with vanadium tetrachloride (VCl4) as a precursor for the realization of programmable memory devices is reported. X-ray diffraction analysis revealed the epitaxial growth of VO2 on c-Al2O3. The phase transition was monitored using resistivity measurements across varying temperatures, demonstrating a decrease of >4 orders of magnitude at the transition temperature, thereby confirming the high quality of the material. From this material, memristive devices are fabricated as resistive random-access memory (RRAM). On the basis of spiking voltage inputs, these RRAM exhibited cycle stability over 512 cycles and state retention stability for >450 s, showing <2% drift. With respect to synaptic-like applications, the RRAM devices were piloted through step patterns to enable multilevel memory states. These ALD-grown VO2-based devices demonstrate potential for use as synaptic connections with multiweight synapses, advancing scalability in neuromorphic applications.

First demonstration of 2T0C-FeDRAM: a-ITZO FET and double gate a-ITZO/a-IGZO FeFET with a record-long multibit retention time of >4-bit and >2000 s.

Conventional DRAM, consisting of one transistor and one capacitor (1T1C), requires periodic data refresh processes due to its limited retention time and data-destructive read operation. Here, we propose and demonstrate a novel 3D-DRAM memory scheme available with a single transistor and a single ferroelectric field-effect transistor (FeFET) DRAM (2T0C-FeDRAM), which offers extended retention time and non-destructive read operation. This architecture uses a back-end-of-line (BEOL)-compatible amorphous oxide semiconductor (AOS) that is suitable for increasing DRAM cell density. Notably, the device structures of a double gate a-ITZO/a-IGZO FeFET, used for data storage and reading, are engineered to achieve an enlarged memory window (MW) of 1.5 V and a prolonged retention time of 104 s. This is accomplished by a double gate and an a-ITZO/a-IGZO heterostructure channel to enable efficient polarization control in hafnium-zirconium oxide (HZO) layers. We present successful program/erase operations of the double gate a-ITZO/a-IGZO FeFET through incremental step pulse programming (ISPP), demonstrating multi-level states with remarkable retention characteristics. Most importantly, we perform 2T0C-FeDRAM operations by electrically connecting the double gate a-ITZO/a-IGZO FeFET and the a-ITZO FET. Leveraging the impressive performance of the double gate a-ITZO/a-IGZO FeFET technology, we have effectively showcased an exceptionally record-long retention time exceeding 2000 s and 4-bit multi-level states, positioning it as a robust contender among emerging memory solutions in the era of artificial intelligence.

Organic heterojunction memristors with enhanced tunable resistive states for artificial synapses

Tunable and uniform evolution of conductance is the key performance metric for neuromorphic computing leveraging memristors. Nonetheless, the stochastic conductance update associated with limited material composition and uncontrollable filament distribution has restricted the tunability that can be customized for targeted synaptic properties. Here, we introduce organic heterojunction memristors utilizing the C60/P3HT bilayer, demonstrating analog switching characteristics with multilevel conductance states. We demonstrate that both conventional bipolar and unipolar voltages can achieve synaptic plasticity modulation for potentiation and depression, offering enhanced tunability. Through in situ Raman spectroscopy and impedance spectroscopy, we directly observe the dynamic alterations within the active layers during switching processes. The reversible migration of ions diminishes the barrier within the polymer layer, leading to highly uniform resistive switching behavior. The C60 layer functions as a confined transport medium, mitigating critical current variability issues. Moreover, we introduce a shunt resistor approach, furnishing analog memristors with selectively adjustable uniformity, enhanced linearity, and expanded dynamic conductance range, providing a general solution adaptable to various memristive hardware architectures.

Light-induced multilevel resistive switching in cesium-doped lead-free halide double perovskite memory device

A series of (CH3NH3)2-xCsxAgBiBr6 films (x=0, 0.1, 0.2, 0.3, 0.4) were fabricated on ITO/glass substrates through the sol-gel method. 15 % Cs doping (x=0.3) was the optimal condition for enhancing the film quality and achieving nonvolatile bipolar resistive switching (BRS) performance in the devices. The on/off ratio in the (CH3NH3)1.7Cs0.3AgBiBr6 (Cs-MAABB-3)-based RS device reached 7.7×104, high and low resistance states were maintained for over 104 s. Under different light intensities with a wavelength of 430 nm, six distinct resistance states were recorded at −0.3 V in the Au/Cs-MAABB-3/ITO/glass device. These multilevel states remained after 5000 s and 650 cycles, indicating the excellent retention and endurance of multilevel RS memory in the Au/Cs-MAABB-3/ITO/glass device. The RS behavior in this device followed the trap-controlled space charge-limited current and conductive filament models. Cs-doping increased the concentration of intrinsic vacancies appropriately, leading to a more prominent RS effect. With varying intensities of illumination, the change of Schottky-like barrier at the Au/Cs-MAABB-3 interface, modulated by the photo-induced carriers, affected the multilevel resistance states in the device. The excellent multilevel RS performance was observed for the first time in the Cs-MAABB-3-based device. It presents broad possibilities for improving high-density memory in lead-free halide double perovskite-based devices.

Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Probabilistic curvature limit states of corroded circular RC bridge columns: Data-driven models and application to lifetime seismic fragility analyses

Reinforcement corrosion has been recognized as an influential factor in the seismic fragility, both demand and capacity models, of aging reinforced concrete (RC) bridges. For capacity models, accurate and applied prediction tools accounting for aging effects are yet to be well established. Current practices usually perform numerical analyses to obtain time-variant capacity models, which are time-consuming particularly when multi-source structural and environmental uncertainties are considered and sometimes even suffer computational non-convergence. To address these issues, this study leverages a rigorously optimized artificial neural network architecture to develop data-driven models for rapid estimates of probabilistic curvature capacity of corroded circular RC bridge columns with flexural failure modes. An extensive database of multi-level curvature limit states (i.e. slight, moderate, severe, and complete) is created through experimentally validated moment–curvature analyses. A new threshold for the moderate limit state is defined based on the strain of core concrete, rather than cover concrete, to account for the potential full erosion of the cover with drastic corrosion. The data-driven probabilistic capacity models are applied to aid the lifetime seismic fragility assessment of a typical highway bridge, where the spectral acceleration at 1.0 s (Sa-10), peak ground velocity (PGV), and Housner intensity (HI) are found consistently, for the first time, as optimal intensity measures for probabilistic demand modeling of RC columns with different extents of aging effects. For the ease of application, the database and code for the data-driven probabilistic capacity models are accessible at https://bit.ly/3uAa8EY .

Multilevel magnetoresistance states in La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 heterostructure grown on MgO

Magnetic tunnel junction (MTJ) is one of the cornerstones of modern information technologies. Bringing MTJ's operation beyond the conventional binary regime, enabled by tunneling magnetoresistance (TMR) effect, is highly promising for prospective memory technologies and neuromorphic hardware development. In this paper, we demonstrate multilevel magnetoresistance states in an all-perovskite-oxide La0.7Sr0.3MnO3 (LSMO)/BaTiO3/LSMO heterostructure grown on MgO substrates. Unlike traditional TMR, we observe four distinct regions of increased magnetoresistance, which result in three magnetic field-induced resistance states in total. We show that the observed phenomenon arises from the low-field magnetoresistance effect, which occurs in the two epitaxial LSMO layers, independently and at different values of the magnetic field. The effect is well simulated by a model based on the presence of structural defects and non-uniform deformations in the LSMO layers, induced by the large lattice mismatch of the LSMO with the MgO substrate. We believe that our findings contribute to the understanding of complex magnetoresistance effects in MTJs and can be taken into consideration for the design of multi-bit memory cells or neuromorphic devices.

Compositional effects of hybrid MoS2–GO active layer on the performance of unipolar, low-power and multistate RRAM device

Currently, 2D nanomaterials-based resistive random access memory (RRAMs) are explored on account of their tunable material properties enabling fabrication of low power and flexible RRAM devices. In this work, hybrid MoS2–GO based active layer RRAM devices are investigated. A facile hydrothermal co-synthesis approach is used to obtain the hybrid materials and a cost-effective spin coating method adopted for the fabrication of Ag/MoS2–GO/ITO RRAM devices. The performance of the fabricated hybrid active layer RRAM device is analysed with respect to change in material properties of the synthesized hybrid material. The progressive addition of 0.5, 1.5, 2.5 and 4.5 weight % of GO to MoS2, results in a hybrid active layer with higher intermolecular interaction, in the case of Ag/MoS2–GO4.5/ITO RRAM device, resulting in a unipolar resistive switching RRAM behavior with low SET voltage of 1.37 V and high I on/I off of 200 with multilevel resistance states. A space charge limited conduction mechanism is obtained during switching, which may be attributed to the trap states present due to functional groups of GO. The increased number of conduction pathways on account of both Ag+ ions and oxygen vacancies (Vo 2+), participating in the formation of conducting filament, results in higher I on/I off. This is the first report of unipolar Ag/MoS2–GO/ITO RRAM devices, which are particularly important in realizing high density crossbar memories for neuromorphic and in-memory computing as well as enabling flexible 2D nanomaterials-based memristor applications.

Implementation of 8-bit reservoir computing through volatile ZrOx-based memristor as a physical reservoir

In this study, we employed a sputtering process to construct a memristive device within the ITO/ZrOx/TaN structure for implementing neuromorphic computing. Initially, we scanned the basic electrical properties of the ITO/ZrOx/TaN device using a DC voltage sweep on the top ITO electrode. A highly uniform gradual resistive switching phenomenon was observed over 100 cycles. The current decay in the low-resistance state was effectively controlled by the volatile memory properties. Gradual conductance changes for potentiation and depression were achieved by applying electrical pulses, enabling the establishment of multi-level conductance states. In addition, the emulation of various synaptic functions was achieved by following the learning rules of SRDP, EPSC, STDP, ADSP, Pavlovian associative learning, and PPF. Finally, 8-bit reservoir computing was demonstrated in cost-effective pattern generation and recognition, highlighting the ITO/ZrOx/TaN device’s advantageous memory storage properties for synaptic characteristics.