Articles tagged with Very Large Scale Integration
A new approach to AI developed by Texas A&M University engineers mimics the human brain's neural processes, integrating learning and memory in a single system. This 'Super-Turing AI' has the potential to revolutionize the industry by reducing energy consumption and environmental impact.
Researchers created a new GIS-based multi-criteria decision tool to guide decisions on using nature-based shorelines or hybrid solutions in the Florida Keys. The study finds that nearly 8% of the shoreline is suitable for nature-based solutions, while 67% is already vegetated or represents another natural shoreline.
A new microscope-integrated OCT system has been developed to identify tumor margins during brain surgery, providing high-resolution images of subsurface anatomy. The system has shown promising results in clinical studies, with the potential to improve outcomes for neurosurgery procedures.
Chong Xie and his team at Rice University have won a $2.9 million grant from the National Institutes of Health to develop an implantable neural electrode system for high-resolution, long-term neural recording and stimulation. The project aims to improve the resolution of existing devices by increasing the density of neurons sampled.
A recent study by the Hebrew University of Jerusalem developed a Free-Standing Microscale Photonic Lantern Spatial Mode (De-)Multiplexer using 3D Nanoprinting. The device enables spatial mode multiplexing, converting between optical waves and separated single-mode signals, with applications in high-capacity communication and imaging.
A massive study identifies new biomarkers for renal cell carcinoma subtypes, improving diagnosis and treatment. The researchers' integrative analysis of proteogenomic datasets reveals molecular features shared by clear cell and non-clear cell RCC tumors, as well as unique features to various subtypes.
A team of Rice University researchers has developed a platform for integrating DNA and RNA data from single-cell sequencing with greater speed and precision. The method, MaCroDNA, relies on a classical algorithm to identify matching pairs of data and outperformed state-of-the-art technologies in accuracy measurements.
The study found that 3D integration can lead to significant heat spreading and crosstalk, reducing heater efficiency by up to -43.3% and increasing thermal crosstalk by up to +44.4%. However, optimizing design variables, such as spacing between µbumps and interconnect linewidth, can minimize the thermal penalty of 3D integration.
Researchers discuss a new approach integrating genomic, epigenomic, transcriptomic, and machine learning methods to identify functional genetic variants and characterize their mode of action in regulating target genes. This method aims to improve understanding of disease etiology and prioritize causative inherited genetic variants.
Researchers successfully fabricate a microlens on a single-mode polarization-stable VCSEL chip using 2-photon-polymerization 3D printing, reducing beam divergence from 14.4° to 3° and enabling compact optical gas sensors with improved performance.
Researchers at OU are developing a scalable, integrated sensing platform to detect and monitor methane emissions in the Anadarko Basin. The system aims to help companies identify and mitigate accidental leaks, achieving detection at the meter scale.
Researchers at EPFL have developed a photonic integrated circuit based erbium-doped amplifier that generates record output power and provides high gain, matching commercial EDFAs. This breakthrough enables new applications in optical communications, LiDAR, quantum sensing, and memories.
Assistant Professor Chenyun Pan is exploring alternative materials and geometric structures to enhance efficiency in intermediate-length interconnects. His research aims to alleviate the limitations of traditional copper-based interconnects and deliver improved chip performance.
Researchers at Tohoku University have developed a nonvolatile microcontroller unit utilizing spintronics-based VLSI design technology, achieving ultra-low power consumption of 47.14μW at 200MHz operation. The MCU's high-performance capabilities make it suitable for IoT sensor node applications.
Materials Design Inc. presents a joint presentation with University of Texas at Dallas, KAUST, and Texas Instruments on the power of atomistic simulation in guiding microelectronics development. The collaboration demonstrates low Vt in CMOS using hybrid cladding layers.