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. Here we circumvent this limitation by designing detachable crystalline antiferromagnetic nanomembranes of α-Fe2O3. First, we show-via transmission-based antiferromagnetic vector mapping-that flat nanomembranes host a spin-reorientation transition and rich topological phenomenology. Second, we exploit their extreme flexibility to demonstrate the reconfiguration of antiferromagnetic states across three-dimensional membrane folds resulting from flexure-induced strains. Finally, we combine these developments using a controlled manipulator to realize the strain-driven non-thermal generation of topological textures at room temperature. The integration of such free-standing antiferromagnetic layers with flat/curved nanostructures could enable spin texture designs via magnetoelastic/geometric effects in the quasi-static and dynamical regimes, opening new explorations into curvilinear antiferromagnetism and unconventional computing.

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. By storing the static DFT/inverse DFT (IDFT) coefficients within the analog arrays, we keep digital computational operations using digital circuits to a minimum. By performing the convolution in reciprocal Fourier space, our approach minimizes connection weight updates, which significantly accelerates both neural network training and interference. Moreover, by minimizing RRAM conductance update frequency, we mitigate the endurance limitations of resistive nonvolatile memories. We show that by leveraging the symmetry and linearity of DFT/IDFTs, we can reduce the power by 1.57 × for convolution over conventional execution. The designed hardware-aware deep neural network (DNN) inference accelerator enhances the peak power efficiency by 28.02 × and area efficiency by 8.7 × over state-of-the-art accelerators. This article paves the way for ultrafast, low-power, compact hardware accelerators.

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 5.64× and area efficiency by 4.7× over state-of-the-art DNN accelerators. Pulse application at the bottom electrodes of the staircase array generates a concurrent input shift, which eliminates the input unfolding, and regeneration required for convolution execution within typical crossbar arrays. Our in-memory compute method operates in charge domain and facilitates high-accuracy floating-point computations with low RRAM states, device requirement. This work provides a path toward fast hardware accelerators that use low power and low area.

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) MoS2 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. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).

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. This work demonstrates a self-consistent physics-based model which incorporates the often-overlooked effects of lattice temperature, vacancy dynamics, and channel electrostatics to accurately solve the interaction between gate potential, ions, and carriers on the memristive switching mechanism. The completed model is carefully calibrated with an ambipolar WSe2 memtransistor and hence enables the investigation of the carrier polarity effect (electrons vs holes) on vacancy transport. Nevertheless, the validity of the model can be extended to different materials by a simple material-dependent parameter modification. Building upon the existing understanding of Schottky barrier height modulation, our study reveals three key insights─leveraging threshold voltage shifts to lower write bias; optimizing lattice temperature distribution and read bias polarity to achieve successful memory state recovery; engineering contact work function to overcome the detrimental parasitic current flow in short channel ambipolar memtransistors. Therefore, understanding the significant correlation between the switching mechanisms, different material systems, and device structures allows performance optimization of operating modes and device designs for future memtransistors-based computing systems.

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 (ABS3 ) is introduced for label-free, low-cost, fully automated, and highly accurate CBD concentrations' classification in a complex biological environment. The ABS3 comprises an array of interdigitated microelectrode sensors, each functionalized with different engineered aptamers. To further empower the functionality of the ABS3 , unique electrochemical features from each sensor are synergized using physics-guided multidimensional analysis. The capabilities of this ABS3 are demonstrated by achieving excellent CBD concentrations' classification with a high prediction accuracy of 99.98% and a fast testing time of 22 µs per testing sample using the optimized random forest (RF) model. It is foreseen that this approach will be the key to the realistic transformation from fundamental research to system miniaturization for diagnostics of disease biomarkers and drug development in the field of chemical/bioanalytics.

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. The 16 channel sensor array extracts spatial and temporal information of the finger movement (deformation) and hand location (proximity) simultaneously. Using machine learning, over 99 and 91% accuracy are achieved for user-independent static and dynamic gesture recognition, respectively. Our approach proves that an extremely simple bimodal sensing platform that identifies local interactions and perceives spatial context concurrently, is crucial in the field of sign communication, remote robotics, and smart manufacturing.

All WSe2 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 WSe2 p-FET, with a solution-processed WSe2 Resistive Random Access Memory. The employed plasma oxidation technique results in a low Schottky barrier height of 25 meV with a mobility of 230 cm2 V-1 s-1, leading to a 100x performance enhanced WSe2 p-FET, while the defective WSe2 Resistive Random Access Memory exhibits a switching energy of 2.6 pJ per bit. Furthermore, guided by our device-circuit modelling, we propose vertically stacked channel FETs for high-density sub-0.01 µm2 memory cells, offering a new beyond-Si solution to enable 3-D embedded memories for future computing systems.

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. The capacitance sensing is a result of E-field line disturbances from electrode deformation (mechanical effect), as well as floating electrodes in the form of human skin (proximity effect). Using the proximity effect, we show that spatial detection as large as 28 cm can be achieved. As a demonstration of a hybrid electronic system, we show that by integrating the IDE capacitors with a capacitance sensing chip, respiration rate due to a human's chest motion can be captured, showing potential in its implementation for wearable health-monitoring.