High-Speed and Low-Energy Resistive Switching with Two-Dimensional Cobalt Phosphorus Trisulfide for Efficient Neuromorphic Computing.

Two-dimensional (2D) materials hold significant potential for the development of neuromorphic computing architectures owing to their exceptional electrical tunability, mechanical flexibility, and compatibility with heterointegration. However, the practical implementation of 2D memristors in neuromorphic computing is often hindered by the challenges of simultaneously achieving low latency and low energy consumption. Here, we demonstrate memristors based on 2D cobalt phosphorus trisulfide (CoPS), which achieve impressive performance metrics including high switching speed (20 ns), low switching energy (1.

Spatially reconfigurable antiferromagnetic states in topologically rich free-standing nanomembranes.

Antiferromagnets hosting real-space topological textures are promising platforms to model fundamental ultrafast phenomena and explore spintronics. However, they have only been epitaxially fabricated on specific symmetry-matched substrates, thereby preserving their intrinsic magneto-crystalline order. This curtails their integration with dissimilar supports, restricting the scope of fundamental and applied investigations.

A Low-Latency DNN Accelerator Enabled by DFT-Based Convolution Execution Within Crossbar Arrays.

Analog resistive random access memory (RRAM) devices enable parallelized nonvolatile in-memory vector-matrix multiplications for neural networks eliminating the bottlenecks posed by von Neumann architecture. While using RRAMs improves the accelerator performance and enables their deployment at the edge, the high tuning time needed to update the RRAM conductance states adds significant burden and latency to real-time system training. In this article, we develop an in-memory discrete Fourier transform (DFT)-based convolution methodology to reduce system latency and input regeneration.

Technology Roadmap for Flexible Sensors.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited.

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch.

Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach-Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components.

Physical Insights into Vacancy-Based Memtransistors: Toward Power Efficiency, Reliable Operation, and Scalability.

Memtransistors that combine the properties of transistor and memristor hold significant promise for in-memory computing. While superior data storage capability is achieved in memtransistors through gate voltage-induced conductance modulation, the lateral device configuration would not only result in high write bias, which compromises the power efficiency, but also suffers from unsuccessful memory reset that leads to reliability concerns. To circumvent such performance limitations, an advanced physics-based model is required to uncover the dynamic resistive switching behavior and deduce the key driving parameters for the switching process.

Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing.

Realization of high-density and reliable resistive random access memories based on two-dimensional semiconductors is crucial toward their development in next-generation information storage and neuromorphic computing. Here, wafer-scale integration of solution-processed two-dimensional MoS memristor arrays are reported. The MoS memristors achieve excellent endurance, long memory retention, low device variations, and high analog on/off ratio with linear conductance update characteristics.

Engineered Nucleotide Chemicapacitive Microsensor Array Augmented with Physics-Guided Machine Learning for High-Throughput Screening of Cannabidiol.

The recent legalization of cannabidiol (CBD) to treat neurological conditions such as epilepsy has sparked rising interest across global pharmaceuticals and synthetic biology industries to engineer microbes for sustainable synthetic production of medicinal CBD. Since the process involves screening large amounts of samples, the main challenge is often associated with the conventional screening platform that is time consuming, and laborious with high operating costs. Here, a portable, high-throughput Aptamer-based BioSenSing System (ABS ) is introduced for label-free, low-cost, fully automated, and highly accurate CBD concentrations' classification in a complex biological environment.

Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition.

As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human-machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybrid-flexible wearable system, consisting of simple bimodal capacitive sensors and a customized low power interface circuit integrated with machine learning algorithms, to accurately recognize complex gestures.

A Low-Power DNN Accelerator Enabled by a Novel Staircase RRAM Array.

Enhancing the ubiquitous sensors and connected devices with computational abilities to realize visions of the Internet of Things (IoT) requires the development of robust, compact, and low-power deep neural network accelerators. Analog in-memory matrix-matrix multiplications enabled by emerging memories can significantly reduce the accelerator energy budget while resulting in compact accelerators. In this article, we design a hardware-aware deep neural network (DNN) accelerator that combines a planar-staircase resistive random access memory (RRAM) array with a variation-tolerant in-memory compute methodology to enhance the peak power efficiency by 5.

All WSe 1T1R resistive RAM cell for future monolithic 3D embedded memory integration.

3D monolithic integration of logic and memory has been the most sought after solution to surpass the Von Neumann bottleneck, for which a low-temperature processed material system becomes inevitable. Two-dimensional materials, with their excellent electrical properties and low thermal budget are potential candidates. Here, we demonstrate a low-temperature hybrid co-integration of one-transistor-one-resistor memory cell, comprising a surface functionalized 2D WSe p-FET, with a solution-processed WSe Resistive Random Access Memory.

A Soft Polydimethylsiloxane Liquid Metal Interdigitated Capacitor Sensor and Its Integration in a Flexible Hybrid System for On-Body Respiratory Sensing.

We report on the dual mechanical and proximity sensing effect of soft-matter interdigitated (IDE) capacitor sensors, together with its modelling using finite element (FE) simulation to elucidate the sensing mechanism. The IDE capacitor is based on liquid-phase GaInSn alloy (Galinstan) embedded in a polydimethylsiloxane (PDMS) microfludics channel. The use of liquid-metal as a material for soft sensors allows theoretically infinite deformation without breaking electrical connections.

Polarity control in WSe2 double-gate transistors.

As scaling of conventional silicon-based electronics is reaching its ultimate limit, considerable effort has been devoted to find new materials and new device concepts that could ultimately outperform standard silicon transistors. In this perspective two-dimensional transition metal dichalcogenides, such as MoS2 and WSe2, have recently attracted considerable interest thanks to their electrical properties. Here, we report the first experimental demonstration of a doping-free, polarity-controllable device fabricated on few-layer WSe2.

Chemically enhanced double-gate bilayer graphene field-effect transistor with neutral channel for logic applications.

In this article, we present the simulation, fabrication, and characterization of a novel bilayer graphene field-effect transistor exhibiting electron mobility up to ~1600 cm(2) V(-1) s(-1), a room temperature I on/I off ≈ 60, and the lowest total charge (~10(11) cm(-2)) reported to date. This is achieved by combined electrostatic and chemical doping of bilayer graphene, which enables one to switch off the device at zero top-gate voltage. Using density functional theory and atomistic simulations, we obtain physical insight into the impact of chemical and electrostatic doping on bandgap opening of bilayer graphene and the effect of metal contacts on the operation of the device.

Sidewall crystalline orientation effect of post-treatments for a replacement metal gate bulk fin field effect transistor.

The crystalline orientation effect is investigated for post-treatments of a replacement metal gate (RMG) p-type bulk fin field effect transistor (FinFET). After post-deposition annealing (PDA) and SF6 plasma treatment, the hole mobility is improved. From low-frequency noise analysis, reduction of the trap density and noise level is observed in PDA- and SF6-plasma-treated devices.