Reconfigurable Logic Research Articles

Reconfigurable Magnetic Inhibitor for Domain Wall Logic and Neuronal Devices.

Spintronic devices based on the electrical manipulation of magnetic chiral domain walls (DWs) within magnetic nanowires promise advanced memory and logic with high speed and density. However, error-free positioning of the DWs along the magnetic nanowires is challenging. Here, we demonstrate reconfigurable domain wall logic and neuronal devices based on the interaction between the DWs and local magnetic inhibitors that are placed in the proximity of the magnetic nanowire. First, we investigate the effect of localized stray fields generated by a nanoscopic magnetic inhibitor on the motion of domain walls moved by current passing through the nanowires. We then show that the localized stray field is sufficient to inhibit or promote the current-induced propagation of chiral DWs depending on the state of the inhibitor. Further, we demonstrate that this allows for a DW-based logic XNOR gate and DW-based neuromorphic devices with leaky integrate-and-fire neuronal functions.

Highly scalable In0.95Ga0.05As based controllable leaky-integrate-fire neuron for high performance spiking neural network and applications

Abstract In this work, we design and simulate a leaky integrate-and-fire (LIF) neuron based on a single Indium Gallium Arsenide (In0.95Ga0.05As) MOSFET. The proposed implementation results in significant improvements in energy, area, and cost reduction. The In0.95Ga0.05As -MOSFET LIF neuron imitates neuron behaviour accurately, with low breakdown voltage, high impact ionization coefficient, and sharp breakdown. Due to the storage of induced holes in the potential well of the In0.95Ga0.05As -MOSFET, impact ionisation is the main mechanism creating the spiking characteristic in this case. It requires only 7.11 pJ/spike of energy, compared to the silicon-based SOI MOSFET, which requires 45 pJ/spike. The LIF neuron exhibits a 0.5 MHz high spiking frequency, which is 5 times higher than real nerves in the human body. Compared to biology, MHz operation provides appealing hardware acceleration. In0.95Ga0.05As -MOSFET LIF neuron firing may be controlled through the application of gate voltage, which can increase the spiking neural networks (SNN) energy efficiency by causing sparse activity. The effects of gate metal work function, Gallium mole fraction, and temperature on spike current variations are also investigated. This neuron circuit has promising potential for large-scale integration in spiking neural network hardware. Reconfigurable threshold logic gates have been investigated for the proposed neuron in order to implement universal digital logic functions, such as NAND and NOR.

Principles and Applications of Two-Dimensional Semiconductor Material Devices for Reconfigurable Electronics

With the advances in edge computing and artificial intelligence, the demands of multifunctional electronics with large area efficiency are increased. As the scaling down of the conventional transistor is restricted by physical limits, reconfigurable electronics are developed to promote the functional integration of integrated circuits. Reconfigurable electronics refer to electronics with switchable functionalities, including reconfigurable logic operation functionalities and reconfigurable responses to electrical or optical signals. Reconfigurable electronics integrate data-processing capabilities with reduced size. Two-dimensional (2D) semiconductor materials exhibit excellent modulation capabilities through electrical and optical signals, and structural designs of 2D material devices achieve versatile and switchable functionalities. 2D semiconductors have great potential to develop advanced reconfigurable electronics. Recent years witnessed the rapid development of 2D material devices for reconfigurable electronics. This work focuses on the working principles of 2D material devices used for reconfigurable electronics, discusses applications of 2D-material-based reconfigurable electronics in logic operation and artificial intelligence, and further provides a future outlook for the development of reconfigurable electronics based on 2D material devices.

Design and Modeling of an Intelligent Robotic Gripper Using a Cam Mechanism with Position and Force Control Using an Adaptive Neuro-Fuzzy Computing Technique

Design and Modeling of an Intelligent Robotic Gripper Using a Cam Mechanism with Position and Force Control Using an Adaptive Neuro-Fuzzy Computing Technique

On-Chip Metamaterial-Enhanced Mid-Infrared Photodetectors with Built-In Encryption Features.

The integration of mid-infrared (MIR) photodetectors with built-in encryption capabilities holds immense promise for advancing secure communications in decentralized networks and compact sensing systems. However, achieving high sensitivity, self-powered operation, and reliable performance at room temperature within a miniaturized form factor remains a formidable challenge, largely due to constraints in MIR light absorption and the intricacies of embedding encryption at the device level. Here, a novel on-chip metamaterial-enhanced, 2D tantalum nickel selenide (Ta₂NiSe₅)-based photodetector, meticulously designed with a custom-engineered plasmonic resonance microstructure to achieve self-powered photodetection in the nanoampere range is unveiled. Gold cross-shaped resonators are demonstrated that generate plasmon-induced ultrahot electrons, significantly enhancing the absorption of MIR photons with energies far below the bandgap and boosting electron thermalization in Ta₂NiSe₅, yielding a 0.1V bias responsivity of 47mA/W-an order of magnitude higher than previously reported values. Furthermore, the implementation of six reconfigurable optoelectronic logic computing ("AND", "OR", "NAND", "NOR", "XOR", and "XNOR") are illustrated via tailored optical and electrical input-output configurations, thereby establishing a platform for real-time infrared-encrypted communication. This work pioneers a new direction in secure MIR communications, advancing the development of high-performance, encryption-capable photonic systems.

Ab-initio investigation on aluminum nitride nanoribbons for reconfigurable logic gates

Ab-initio investigation on aluminum nitride nanoribbons for reconfigurable logic gates

All-Fiber Synapse Based on Ge2Sb2Te5 for Reconfigurable Logic Operations

All-Fiber Synapse Based on Ge₂Sb₂Te₅ for Reconfigurable Logic Operations

Cognitive Computing Approaches A Comprehensive Performance Evaluation Using the Weighted Property Method

In an era of rapid technological advancement, cognitive computing is emerging as a transformative paradigm that integrates methods from psychology, biology, signal processing, and mathematics to create intelligent computational systems. This research uses the Weighted Property Methodology (WPM) to comprehensively evaluate various cognitive computing approaches, analyzing their performance on important dimensions including ethical and social implications, learning rate, usability, and decision support. This study systematically examines five cognitive computing approaches: brain-inspired, human-like, adaptive, intelligent, and intelligent computing. Through rigorous multi-criteria analysis, brain-inspired computing distinguished itself as the best-performing approach, achieving a maximum priority score of 0.86755. The ability of this approach to model computational processes after human neural structures demonstrates significant potential for advanced technological development. Key findings reveal that cognitive computing is not a single concept but a complex ecosystem of interconnected methods. Human-like computing came in second, emphasizing the critical role of human-centered design in technological innovation. Adaptive computing came in third, highlighting the importance of dynamic computational reconfiguration. The research underscores the critical role of cognitive computing in addressing contemporary challenges in domains such as healthcare, web development, and decision support systems. By simulating human-like cognitive processes, these approaches offer unprecedented capabilities in processing complex, multidimensional data and making intelligent, context-aware decisions. This comprehensive analysis provides valuable insights into the evolving landscape of cognitive computing, and provides a roadmap for future research and technological innovation in a data-driven world.

Systematic Analysis of Memristor Theory, manufacture and application status

Research into memristor technology has gained significant momentum because of its ability to revolutionize storage and neuromorphic computing. Memristors are two-terminal devices that retain an electrical resistance state based on prior voltage applications, embodying a nonlinear element suited for future electronic systems. Because they have low energy requirements, rapid switching capabilities, and ability to store multiple states, memristors are well-positioned to significantly influence the development of cutting-edge memory solutions and serve as artificial synapses within neuromorphic computing systems. This paper provides an exploration of recent advancements of the memristor technology, focusing on material composition, switching mechanisms, and their applications such as non-volatile memory, reconfigurable logic gates, and neuromorphic computing systems. By analyzing materials such as binary oxides, perovskites, and two-dimensional compounds, the review delves into how these materials enhance performance characteristics like speed, endurance, and energy efficiency. The discussion underscores the transformative potential of memristors in shaping next-generation computing architectures, making them a cornerstone of future innovations in both digital and analog computing.

MEGA12: Molecular Evolutionary Genetic Analysis version 12 for adaptive and green computing.

We introduce the 12th version of the Molecular Evolutionary Genetics Analysis (MEGA) software. This latest version brings many significant improvements by reducing the computational time needed for selecting optimal substitution models and conducting bootstrap tests on phylogenies using maximum likelihood (ML) methods. These improvements are achieved by implementing heuristics that minimize likely unnecessary computations. Analyses of empirical and simulated datasets show substantial time savings by using these heuristics without compromising the accuracy of results. MEGA12 also implements an evolutionary sparse learning approach to identify fragile clades and associated sequences in evolutionary trees inferred through phylogenomic analyses. In addition, this version includes fine-grained parallelization for ML analyses, support for high-resolution monitors, and an enhanced Tree Explorer. MEGA12 can be downloaded from https://www.megasoftware.net.

Soft Electronic Switches and Adaptive Logic Gates Based on Nanostructured Gold Networks

AbstractThe advent of neuromorphic substrates is promoting the development of in materia autonomous and adaptive devices, employed as hardware solutions to reduce the current inefficiencies of traditional data processing techniques, in terms of energy requirements. The integration of data processing capabilities on soft materials is here focused on the development of the edge computing paradigm of interest for soft robotics and wearable devices. For such purposes, gold nanostructured complex networks produced in the gas phase are employed to fabricate neuromorphic devices. The integration of the latter on a soft Polydimethylsiloxane (PDMS) substrate equipped with stretchable laser‐induced graphene electrodes, is exploited for the production of in materia devices to bridge the gap between data processing and interaction with the environment. The description and the control of the non‐linear, resistive switching electrical properties are demonstrated by the development of soft mechano‐responsive electronic switches and soft reconfigurable logic gates. These preserve Boolean functions classifications even under small mechanical perturbations, thanks to the redundant and adaptive connectivity of the gold networks. These results constitute a promising starting point for a fruitful combination of physical and computing intelligence directly integrated on soft systems to efficiently interact with the surrounding scenario.

Resistive switching behavior of quasi-2D CsPbBr3 memristors: Impact of ambient atmosphere on logic storage and computing integration with anti-crosstalk and reconfiguration features

Passive units integrating storage and computing with anti-crosstalk and multi-logic reconstruction are crucial for high computing power and high-density non-volatile storage. In this study, we report an anti-crosstalk and reconfigurable logic memory based on a single passive quasi-two-dimensional (2D) CsPbBr3 device. The effect of the ambient atmosphere (air and N2 environments) on the resistive behavior of the memristors is explored. In air, these devices exhibit negative differential resistance (NDR) effects and antipolar resistive switching behavior, while in N2, they display irreversible switching from low-resistance state to high-resistance state. Various active electrodes (Ag, Cu, Au, and C) were employed to investigate this phenomenon. It is proposed that in air, O ions interact with surface defects under high alternating voltage, retaining a significant quantity of Br− ions within the quasi-2D CsPbBr3, resulting in capacitive-like behavior. Conversely, in N2, surface defects capture Br− ions, leading to the absence of a hysteresis loop in the I-V characteristic. Under N2 operation, write-once-read-many (WORM) capability is achieved. Surprisingly, operating under air enables integrated non-volatile storage and computing, facilitating 12 reconfigurable logic operations in a passive 1R structure and suppressing sneak current in crosstalk setups. This study emphasizes the pivotal role of air in the resistive switching mechanism and provides novel insights for developing next-generation memories tailored for high-density integrated circuits and storage-computing integration.

Adaptive In-Sensor Computing for Enhanced Feature Perception and Broadband Image Restoration.

Traditional imaging systems struggle in weak or complex lighting environments due to their fixed spectral responses, resulting in spectral mismatches and degraded image quality. To address these challenges, a bioinspired adaptive broadband image sensor is developed. This innovative sensor leverages a meticulously designed type-I heterojunction alignment of 0D perovskite quantum dots (PQDs) and 2D black phosphorus (BP). This configuration enables efficient carrier injection control and advanced computing capabilities within an integrated phototransistor array. The sensor's unique responses to both visible and infrared (IR) light facilitate selective enhancement and precise feature extraction under varying lighting conditions. Furthermore, it supports real-time convolution and image restoration within a convolutional autoencoder (CAE) network, effectively countering image degradation by capturing spectral features. Remarkably, the hardware responsivity weights perform comparably to software-trained weights, achieving an image restoration accuracy of over 85%. This approach offers a robust and versatile solution for machine vision applications that demand precise and adaptive imaging in dynamic lighting environments.

Reconfigurable skyrmion logic gates and diodes in the same synthetic antiferromagnetic nanotrack based on potential well inducting effect

Abstract Magnetic skyrmions are nanoscale spin configurations with topological protection properties, which have broad application potential in the next generation of spintronic devices. Here, we report on the current-driven dynamics of skyrmions in synthetic antiferromagnetic nanotracks with voltage-controlled magnetic anisotropy. This research reveals that, compared to a single skyrmion, when two skyrmions are createdsimultaneously, the inducting effect of the potential well generated by the voltage gateon the skyrmions is partially counteracted by the interaction between the skyrmions,resulting in a reduction in the critical current required for the skyrmions to pass thevoltage gate. Moreover, the critical current required for the forward moving skyrmions to depin from the voltage gate is significantly lower than that required for the reverse moving skyrmions. Based on the dynamic behavior of skyrmions, we have proposed and achieved the skyrmion logic AND, OR, NOT, NAND, NOR gates and the diodes on the same synthetic antiferromagnetic nanotrack by micromagnetic simulation, in which the logic NOT, NAND, and NOR gates are realized in a reconfigurable way. Our results are beneficial for the design and development of non-volatile spintronic devices with integrated multifunctionality and ultra-low energy consumption.

Reconfigurable Artificial Perception System and Logic Gates Based on Large‐Scale Antiambipolar 2D Heterostructure Array

AbstractVan der Waals (vdWs) heterostructures enable the fabrication of various optoelectronic devices with unprecedented functionalities, particularly antiambipolar transistors. Different methods have been employed to construct such devices; however, their integration and versatility remain significantly constrained. Here, large‐scale antiambipolar vdWs heterostructure arrays capable of implementing self‐adaptive artificial perception systems and reconfigurable logic gates are verified adopting 2D Te/WS2 heterojunctions for the first time. The resulting transistors allow the modulation of band slope and depletion regions in individual constituents through electrostatic gating, delivering a high rectification ratio of 1.8 × 104. Benefiting from the gate‐tunable antiambipolar characteristic and charge capture, such a device can realize reconfigurable modulation of synaptic plasticity between excitatory and inhibitory modes, enabling the emulation of artificial perception system for the adaptive and subjective perception adjustment in various external environments and also a recognition accuracy of 90.5% for image classification through training and inference simulations. In addition, five fundamental logic gate operations with good stability and reliability, as well as low energy consumption, are achieved in the dual‐gate antiambipolar transistor by tuning the input signals. Furthermore, similar antiambipolar characteristics are also observed from other 2D Te‐based heterostructures, defining a simple, effective, and universal method to create multifunctional antiambipolar transistors.

Anti-Interference All-Optical Logic Computing Based on a 2D Polarization-Sensitive Photodiode.

Optical logic operation is promising for ultrafast information processing and optical computing due to the high computation speed and low power consumption. However, conventional optical logic devices require either a complex structure and circuit design or a constant voltage supply, which impedes the development of high-density integrated circuits. Here, all-optical logic devices are designed using a self-powered polarization-sensitive photodiode of the GeSe homojunction, which is attributed to an anisotropic band structure and built-in electric field. The single photodiode can realize linear logic functions (AND, OR, NAND) and a nonlinear logic gate (XOR) by programming wavelength, power density, and polarization angle. Moreover, complex logic functions (XNOR, Y = IN1, Y = IN2, Y = IN1, and Y = IN2) can be achieved through integrating two photodiodes in parallel. In addition, the neural network algorithm is utilized to validate the feasibility of all-optical logic computing and anti-interference. This work proposes an avenue to design an all-optical reconfigurable logic operation in polarization-sensitive devices.

Applying Run-Length Compression to the Configuration Data of SLM Fine-Grained Reconfigurable Logic

Applying Run-Length Compression to the Configuration Data of SLM Fine-Grained Reconfigurable Logic

Microscale droplet assembly enables biocompatible multifunctional modular iontronics.

Hydrogel iontronic devices can emulate biological functions and communicate with living matter. But the fabrication of miniature, soft iontronic devices according to modular designs has not been achieved. In this work, we report the use of surfactant-supported assembly of freestanding microscale hydrogel droplets to construct various iontronic modules, circuits, and biointerfaces. Chemical modifications of silk fibroin produced a pair of oppositely charged hydrogels. Microscale assembly of various combinations of hydrogel droplets produced iontronic diodes, npn- and pnp-type transistors, and diverse reconfigurable logic gates. Through the incorporation of poly(amino acid)s, we have demonstrated a droplet-based synthetic synapse with ionic polymer-mediated long-term plasticity. Further, our iontronic transistor can serve as a biocompatible sensor to record electrophysiological signals from sheets of human cardiomyocytes, paving a way to the building of miniature bioiontronic systems.

Bidirectional Polarization-Integrated Van Der Waals Ferroelectric Field-Effect Transistor for Multi-State Storage and Dual-Logic-in-Memory Computing

The rapidly growing number of AI-enabled applications is driving the need for development of energy efficient computing hardware that can handle massive amount of computations required for neural network operation. As one of the promising approaches for processing data within memory, Logic-in-memory is parallel execution of reconfigurable logic operation within memory allows fast and efficient computations. For realizing the logic-in-memory hardware, implementing practical logic functions with high density is of critical concern.Here, we demonstrate a multi-state dual-logic-in-memory device implemented via a single bidirectional polarization-integrated ferroelectric field-effect transistor (BPI-FeFET) by integrating the in-plane vdW ferroelectric semiconductor SnS and the out-of-plane vdW ferroelectric gate dielectric CuInP2S6 (CIPS). Four reliable resistance/conductance states (two-bit storage) with an on/off ratio between the highest and the lowest channel current of >105, high switching endurance (>104 cycles), and stable retention characteristics (>104 s). The mechanism of two-bit storage in a single BPI-FeFET is analyzed from two perspectives: carrier density and carrier injection controls, originating from the out-of-plane polarization of the gate dielectric and in-plane polarization of the semiconductor, respectively.Unlike the conventional multilevel FeFET, direct conversions among the four-memory states of proposed BPI-FeFET without pre-examination or additional erasing step are demonstrated experimentally. The potential of the BPI-FeFET as a low-cost and high-density in-memory computing circuit was further explored. To utilize the fabricated BPI-FeFET as a dual-logic-in-memory device, two logical operations are selected (e.g. XOR and AND) by properly setting initial states, and their parallel execution is demonstrated. Additionally, these logical computations are reconfigurable when the initial state of the device is set to different states, making it flexible for implementing various types of functions that can be potentially required for different types of neural networks. We believe the flexibility and efficiency of dual-logic-in-memory devices provide a promising pathway for realizing next generation low power computing systems.

Symmetry engineering in 2D bioelectronics facilitating augmented biosensing interfaces

Symmetry lies at the heart of two-dimensional (2D) bioelectronics, determining material properties at the fundamental level. Breaking the symmetry allows emergent functionalities and effects. However, symmetry modulation in 2D bioelectronics and the resultant applications have been largely overlooked. Here, we devise an oxidized architectural MXene, referred to as oxidized MXene (OXene), that couples orbit symmetric breaking with inverse symmetric breaking to entitle the optimized interfacial impedance and Schottky-induced piezoelectric effects. The resulting OXene validates applications ranging from microelectrode arrays, gait analysis, active transistor matrix, and wireless signaling transmission, which enables high-fidelity signal transmission and reconfigurable logic gates. Furthermore, OXene interfaces were investigated in both rodent and porcine myocardium, featuring high-quality and spatiotemporally resolved physiological recordings, while accurate differentiated predictions, enabled via various machine learning pipelines.