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: Sept. 11


Non-toxic photoluminescent nanoparticles Researchers from Osaka University developed a way to improve display technologies using non-toxic light-emitting nanoparticles. In trying to replace cadmium and other toxic materials used in quantum dots, scientists have turned to non-toxic nanoparticles that emit light in an efficient manner by creating I–III–VI semiconductors, such as silver in... » read more

Power/Performance Bits: Aug. 21


Physical neural network Engineers at UCLA built a physical artificial neural network capable of identifying objects as light passes through a series of 3D printed polymer layers. Called a "diffractive deep neural network," it uses the light bouncing from the object itself to identify that object, a process that consumes no energy and is faster than traditional computer-based methods of imag... » read more

Power/Performance Bits: July 31


Training optical neural networks Researchers from Stanford University used an optical chip to train an artificial neural network, a step that could lead to faster, more efficient AI tasks. Although optical neural networks have been recently demonstrated, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical... » read more

The Growing Materials Challenge


By Katherine Derbyshire & Ed Sperling Materials have emerged as a growing challenge across the semiconductor supply chain, as chips continue to scale, or as they are utilized in new devices such as sensors for AI or machine learning systems. Engineered materials are no longer optional at advanced nodes. They are now a requirement, and the amount of new material content in chips contin... » read more

System Bits: April 17


Smartphone microscopes transformed into lab-grade devices with deep learning UCLA Samueli School of Engineering researchers have demonstrated that deep learning techniques can discern and enhance microscopic details in photos taken by smartphones in order to improve the resolution and color details of smartphone images so much that they approach the quality of images from laboratory-grade mic... » read more

Manufacturing Bits: April 3


World's brightest accelerator Japan’s High Energy Accelerator Research Organization (KEK) is readying what is considered the world’s most luminous or brightest particle accelerator. The system, dubbed the SuperKEKB, combines an electron-positron collider with a new and advanced detector. The storage ring system is designed to explore and measure rare decays of elementary particles, such... » read more

System Bits: Dec. 12


Increasing performance scaling with packageless processors Demand for increasing performance is far outpacing the capability of traditional methods for performance scaling. Disruptive solutions are needed to advance beyond incremental improvements. Traditionally, processors reside inside packages to enable PCB-based integration. However, a team of researchers from the Department of Electrical ... » read more

Get Ready For In-Mold Electronics


Imagine inserting the electronics into a product without using a printed circuit board, a module, or even a system-in-package. That's the promise of in-mold electronics (IME), a technology that has been around for years, but which is just beginning to see wider adoption. The technology is related to conductive inks and transparent conductive films. The IME manufacturing process is said to pr... » read more

Manufacturing Bits: Aug. 15


Self-collapse lithography The University of California at Los Angeles (UCLA) has developed a technology called self-collapse lithography. The technology, reported in the journal Nano Letters, resembles the combination of nanoimprint, selective removal and a chemical lift-off process. More specifically, though, the technology provides insights into patterning using a chemical lift-off lith... » read more

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