Manufacturing Bits: Nov. 20


Predicting crystal structures A group of researchers have improved a crystal structure prediction algorithm, enabling the ability to develop new crystal structures and compounds at faster rates. In 2005, Artem Oganov, now a professor at the Skolkovo Institute of Science and Technology (Skoltech) and the Moscow Institute of Physics and Technology (MIPT), developed a crystal structure predic... » read more

Power/Performance Bits: Nov. 20


In-memory compute accelerator Engineers at Princeton University built a programmable chip that features an in-memory computing accelerator. Targeted at deep learning inferencing, the chip aims to reduce the bottleneck between memory and compute in traditional architectures. The team's key to performing compute in memory was using capacitors rather than transistors. The capacitors were paire... » read more

System Bits: Nov. 20


Designing transistors that don’t overheat In order to avoid heat-induced voids and cracking that can cause chips and circuits to fail, Stanford University and University of California at Davis researchers have developed a way to not only manage heat, but help route it away from delicate devices that leverages a thermal transistor, which is a nanoscale switch that can conduct heat away from ... » read more

Manufacturing Bits: Nov. 13


Quantum memories The University of Alberta has developed a new method for making quantum memories, paving the way for a next-generation quantum Internet. Quantum memory is targeted for quantum networks and computers. In classical computing, the information is stored in bits, which can be either a “0” or “1”. In quantum computing, information is stored in quantum bits, or qubits, whi... » read more

System Bits: Nov. 13


Deep learning device identifies airborne allergens To identify and measure airborne biological particles, or bioaerosols, that originate from living organisms such as plants or fungi, UCLA researchers have invented a portable device that uses holograms and machine learning. The device is trained to recognize five common allergens — pollen from Bermuda grass, oak, ragweed and spores from t... » read more

Power/Performance Bits: Nov. 13


ML identifies LED material Researchers at the University of Houston created a machine learning algorithm that can predict a material's properties to help find better host material candidates for LED lighting. One recommendation was synthesized and tested. The technique, a support vector machine regression model, was efficient enough to run on a personal computer. It scanned a list of 118,28... » read more

Manufacturing Bits: Nov. 6


FISH metrology The University of Illinois at Urbana-Champaign and the Mayo Clinic have developed a new molecular probe for use in imaging cells in living organisms. The probe combines conventional fluorescence in situ hybridization (FISH) metrology techniques with compact quantum dots. This technology can measure and count ribonucleic acid (RNA) in cells and tissue without organic dyes. ... » read more

System Bits: Nov. 6


Keeping data private To preserve privacy during data collection from the Internet, Stanford University researchers have developed a new technique that maintains personal privacy given that the many devices part of our daily lives collect information about how we use them. Stanford computer scientists Dan Boneh and Henry Corrigan-Gibbs created the Prio method for keeping collected data priva... » read more

Power/Performance Bits: Nov. 6


Camera for object recognition Researchers from the University of Illinois at Urbana-Champaign developed a new camera that could improve object detection in vehicles. Inspired by the visual system of mantis shrimp, the camera detects the polarization of light and has a dynamic range about 10,000 times higher than today's commercial cameras. "In a recent crash involving a self-driving car, th... » read more

Manufacturing Bits: Oct. 30


World’s smallest gyroscope The California Institute of Technology has developed the world's smallest optical gyroscope. The gyroscope is 500 times smaller than current devices, but it can detect phase shifts that are 30 times smaller than today’s systems. [caption id="attachment_24139584" align="alignleft" width="300"] The new optical gyroscope—shown here with grains of rice—is 5... » read more

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