Research Bits: Oct. 28


Mushroom memristors Researchers from The Ohio State University found that common edible mushrooms can be grown and trained to act as organic memristors. The team cultured samples of shiitake and button mushrooms, dehydrated them once mature to ensure long-term viability, connected them to special electronic circuits, and then electrocuted them at various voltages and frequencies. “Myce... » read more

Chip Industry Week in Review


SEMICON West was held in Phoenix this week, with presentations covering heterogeneous integration, AI, quantum, supply chain resilience, and more. Amid the buzz of the conference, some key manufacturing and test announcements were made this week: The strategic importance of the Phoenix area hub was highlighted. Amkor Technology broke ground this week on its advanced packaging and test camp... » read more

Research Bits: Oct. 7


Doping oxide insulator improves SiGe conductivity Researchers from TU Wien, Johannes Kepler University Linz, and TU Bergakademie Freiberg manufactured a silicon-germanium (SiGe) transistor using an alternative approach that involves doping the insulating oxide layer to produce a long-range effect that extends into the semiconductor. Called modulation acceptor doping (MAD), the technique ena... » read more

Chip Industry Week In Review


Infineon rolled out the world's first 300mm gallium nitride (GaN) wafer, opening the door for high-volume manufacturing of GaN-based power semiconductors. A 300mm wafer contains 2.3 times as many chips per wafer as a 200mm wafer. Fig.1: Infineon's 300mm GaN wafer. Source: Infineon The Semiconductor Industry Association released its 2024 State of the U.S. Semiconductor Industry report th... » read more

Chip Industry’s Technical Paper Roundup: Nov. 1


New technical papers added to Semiconductor Engineering’s library this week. [table id=61 /] » read more

New Class of Electrically Driven Optical Nonvolatile Memory


A new technical paper titled "Electrical Programmable Multi-Level Non-volatile Photonic Random-Access Memory" was published by researchers at George Washington University, Optelligence, MIT, and the University of Central Florida. Researchers demonstrate "a multi-state electrically-programmed low-loss non-volatile photonic memory based on a broadband transparent phase change material (Ge2Sb2S... » read more

Data-driven RRAM device models using Kriging interpolation


New technical paper from The George Washington University and NIST with support from DARPA and others. Abstract "A two-tier Kriging interpolation approach is proposed to model jump tables for resistive switches. Originally developed for mining and geostatistics, its locality of the calculation makes this approach particularly powerful for modeling electronic devices with complex behavior la... » read more

Power/Performance Bits: Sept. 8


Backscatter radios for 5G Researchers at the Georgia Institute of Technology, Nokia Bell Labs, and Heriot-Watt University propose using backscatter radios to support high-throughput communication and 5G-speed Gb/sec data transfer using only a single transistor. “Our breakthrough is being able to communicate over 5G/millimeter-wave (mmWave) frequencies without actually having a full mmWave... » read more

Power/Performance Bits: March 16


Adaptable neural nets Neural networks go through two phases: training, when weights are set based on a dataset, and inference, when new information is assessed based on those weights. But researchers at MIT, Institute of Science and Technology Austria, and Vienna University of Technology propose a new type of neural network that can learn during inference and adjust its underlying equations to... » read more

Power/Performance Bits: May 19


Neuromorphic magnetic nanowires Researchers from the University of Texas at Austin, University of Texas at Dallas, and Sandia National Laboratory propose a neuromorphic computing method using magnetic components. The team says this approach can cut the energy cost of training neural networks. "Right now, the methods for training your neural networks are very energy-intensive," said Jean Ann... » read more

← Older posts