Research Bits: Feb. 21


High-quality ‘chirps’ for automotive, industrial mmWave radar Imec demonstrated a low-power phase-locked loop (PLL) that generates high-quality frequency-modulated continuous-wave (FMCW) signals for mmWave radar, which can be used in short-range automotive and industrial radar applications. The FMCW radars popular in healthcare, automotive, and industrial send out sinusoidal waves that get... » read more

The Good And Bad Of Bi-Directional Charging


Auto OEMs are starting to offer bi-directional charging in EVs, allowing batteries to power homes during outages or wherever else it is needed, and to smooth out any hiccups in the grid. But this technology also can shorten the lifetime of batteries, and it can open the door to more cyberattacks. The idea behind bi-directional charging is simple enough. EVs can store huge amounts of power, a... » read more

Research Bits: Nov. 1


Atomic-level rare earth manipulation Scientists from Ohio University, Argonne National Laboratory, and the University of Illinois at Chicago have rotated a single, charged rare earth molecule on a metal surface without changing the charge. The team used scanning tunneling microscopy (STM) system to rotate a positively charged Europium base molecule with negatively charged counterions as a p... » read more

Research Bits: Aug. 23


Algae-powered microprocessor Engineers from the University of Cambridge, Arm Research, Scottish Association for Marine Science, and Norwegian University of Science and Technology used a widespread species of blue-green algae to power an Arm Cortex M0+ microprocessor continuously for over a year. The algae, Synechocystis, is non-toxic and harvests energy from photosynthesis. The tiny electri... » read more

Technical Paper Round-Up: June 21


New technical papers added to Semiconductor Engineering’s library this week. [table id=34 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit f... » read more

Graphene Nanoribbon Transistors Using Hydrocarbon Seeds (University of Wisconsin-Madison)


New research paper titled "Graphene nanoribbons initiated from molecularly derived seeds" from researchers at University of Wisconsin-Madison with contributions from Argonne National Laboratory. Abstract "Semiconducting graphene nanoribbons are promising materials for nanoelectronics but are held back by synthesis challenges. Here we report that molecular-scale carbon seeds can be exploi... » read more

Technical Paper Round-up: June 14


New technical papers added to Semiconductor Engineering’s library this week. [table id=33 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit f... » read more

AlphaGo Game Influences Argonne’s New AI Tool For Materials Discovery


Research paper titled "Learning in continuous action space for developing high dimensional potential energy models" from researchers at Argonne National Lab with contributions from Oak Ridge National Laboratory. Abstract "Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action ... » read more

Argonne & Univ. of Chicago: Using Quantum Computers to Simulate Quantum Materials


Research study titled "Simulating the electronic structure of spin defects on quantum computers," by Argonne National Laboratory and the University of Chicago. Abstract: "We present calculations of the ground and excited state energies of spin defects in solids carried out on a quantum computer, using a hybrid classical/quantum protocol. We focus on the negatively charged nitrogen vacancy c... » read more

Research Bits: April 19


Processor power prediction Researchers from Duke University, Arm Research, and Texas A&M University developed an AI method for predicting the power consumption of a processor, returning results more than a trillion times per second while consuming very little power itself. “This is an intensively studied problem that has traditionally relied on extra circuitry to address,” said Zhiy... » read more

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