Electrically Robust Single-Crystalline WTe2 Nanobelts for Nanoscale Electrical Interconnects

As the elements of integrated circuits are downsized to the nanoscale, the current Cu-based interconnects are facing limitations due to increased resistivity and decreased current-carrying capacity because of scaling. Here, the bottom-up synthesis of single-crystalline WTe2 nanobelts and low- and high-field electrical characterization of nanoscale interconnect test structures in various ambient conditions are reported. Unlike exfoliated flakes obtained by the top-down approach, the bottom-up growth mode of WTe2 nanobelts allows systemic characterization of the electrical properties of WTe2 single crystals as a function of channel dimensions. Using a 1D heat transport model and a power law, it is determined that the breakdown of WTe2 devices under vacuum and with AlOx capping layer follows an ideal pattern for Joule heating, far from edge scattering. High-field electrical measurements and self-heating modeling demonstrate that the WTe2 nanobelts have a breakdown current density approaching approximate to 100 MA cm(-2), remarkably higher than those of conventional metals and other transition-metal chalcogenides, and sustain the highest electrical power per channel length (approximate to 16.4 W cm(-1)) among the interconnect candidates. The results suggest superior robustness of WTe2 against high-bias sweep and its possible applicability in future nanoelectronics

A Resource-Efficient Keyword Spotting System Based on a One-Dimensional Binary Convolutional Neural Network

This paper proposes a resource-efficient keyword spotting (KWS) system based on a convolutional neural network (CNN). The end-to-end KWS process is performed based solely on 1D-CNN inference, where features are first extracted from a few convolutional blocks, and then the keywords are classified using a few fully connected blocks. The 1D-CNN model is binarized to reduce resource usage, and its inference is executed by employing a dedicated engine. This engine is designed to skip redundant operations, enabling high inference speed despite its low complexity. The proposed system is implemented using 6895 ALUTs in an Intel Cyclone V FPGA by integrating the essential components for performing the KWS process. In the system, the latency required to process a frame is 22 ms, and the spotting accuracy is 91.80% in an environment where the signal-to-noise ratio is 10 dB for Google speech commands dataset version 2

Synthesis of Novel Dihydropyridothienopyrimidin-4,9-dione Derivatives

A novel molecular scaffold, dihydropyridothienopyrimidin-4,9-dione, was synthesized from benzylamine or p-methoxybenzylamine in six steps involving successive ring closure to form a fused ring system composed of dihydropyridone, thiophene and pyrimidone. The pharmacological versatility of the dihydropyridothenopyrimidin-4,9-dione scaffold was demonstrated by inhibitory activity against metabotropic glutamate receptor subtype 1 (mGluR1), which shows that the title compounds can serve as an interesting scaffold for the discovery of potential bioactive molecules for the treatment of human diseases