Superconducting nanocircuits, which are usually fabricated from superconductor films, are the core of superconducting electronic devices. While emerging transition-metal dichalcogenide superconductors (TMDSCs) with exotic properties show promise for exploiting new superconducting mechanisms and applications, their environmental instability leads to a substantial challenge for the nondestructive preparation of TMDSC nanocircuits. Here, we report a universal strategy to fabricate TMDSC nanopatterns via a topotactic conversion method using prepatterned metals as precursors. Typically, robust NbSe2 meandering nanowires can be controllably manufactured on a wafer scale, by which a superconducting nanowire circuit is principally demonstrated toward potential single photon detection. Moreover, versatile superconducting nanocircuits, e.g., periodical circle/triangle hole arrays and spiral nanowires, can be prepared with selected TMD materials (NbS2, TiSe2, or MoTe2). This work provides a generic approach for fabricating nondestructive TMDSC nanocircuits with precise control, which paves the way for the application of TMDSCs in future electronics.

Machine learning (ML) algorithms are being adopted to analyze medical data in specialties like radiology, oncology, and cardiology, promising faster interpretation with accuracy close to doctors' diagnostics. [1] The next frontier in computing technology is to bring these powerful algorithms to implantable medical devices, which requires automation of real-time life-saving therapeutic decisions without the physician's presence. An example is the need for improved medical solutions for life-saving cardiac defibrillation therapies, that can detect bioelectric anomalies (e.g., cardiac arrhythmias) and act on this data locally for real-time therapy delivered within tens of seconds or minutes since the onset of life-threatening ventricular fibrillation (VF). The statistics put this challenging technological need in perspective: ventricular arrhythmias such as VF are responsible for over 700 000 sudden cardiac deaths a year in the USA and Europe. [2] VF is a common, life-threatening arrhythmia characterized by chaotic asynchronous electrical activity of the cardiac muscle, which results in death within 10 minutes.Individual differences in physiological mechanisms, anatomic and genetic determinants, and etiologies of various arrhythmias impact the course of treatment. Ablation therapy, while promising, remains a work in progress. Therefore, on average, defibrillation therapy delivered by implantable cardioverter defibrillators (ICDs) remains the most effective treatment as antiarrhythmic drugs have limited efficacy and can be associated with adverse side effects. Implants have to be biocompatible, organ conformal, and small enough to minimize the tissue damage and be capable of independent autonomous operation without external intervention. Low power is an essential characteristic to avoid the heat damage to the tissue and prolong the lifetime of the embedded battery for many years without recharging. [3] Currently, most volume of the ICD has been occupied by batteries, which has limited the volume reduction and the computing capacity. ICD has local computing based on a microprocessor to detect and differentiate arrhythmia to offer different treatments, but the resolution provided by ICD is really low typically limited to only one or a couple of sensors; as such, the ability to detect arrhythmia wavefronts is non-existent. The data can be read wirelessly by the physician during periodic checkups. Increasing the sensing resolution is desired but the local computing capacity has to also be increased which is difficult due to power constraints. Wireless data transmission for processing of data outside of the body is not a viable solution either, as real-time data transfer between

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Although many multiplexed arrays of a superconducting nanowire single-photon detector (SNSPD) have been reported, it is still a major challenge to develop pixel-level readout arrays with high efficiency, parallel detection, and fast processing for real-time imaging. Here, we report a SNSPD array with inductor-shaping pulses for approaching the pixel-level readout. Optimized inductors are introduced to shape the output pulses of each pixel, and the response pulses of all pixels are synthesized in a series-connected structure. Then, the on/off states of all pixels can be encoded to the widths, amplitudes, and areas of the output pulses by the single-channel readout. This proposal is verified by a 4-pixel SNSPD array and a 16-pixel SNSPD array. It shows that the array not only inherits the features of the single-pixel SNSPD, such as photosensitive area, filling factor, quantum efficiency, and dark count rate, but also implements parallel operation of all pixels, which is always confused in traditional multiplexed SNSPD arrays. At the same time, the single-channel readout simplifies the system, and the serial digital signal converted from the shaped pulse enabled an easy and fast readout process, paving the way for high performance and real-time imaging.

Cellular Neural Networks In article number http://doi.wiley.com/10.1002/aisy.202200032, Gina C. Adam and co‐workers present a closed‐loop distributed solution based on cellular neural network algorithms to detect abnormal wavefronts in cardiac signals recorded in human tissue. A chiplet‐based hardware implementation using memristors could support future low energy and painless cardiac defibrillation implants, as well as other medical technologies that require compact and efficient computing.