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Analyzing Electro-Photonic Systems


The design and analysis of electro-optical systems is pushing tools into the complex multi-physics domain, making it challenging to create models that execute at reasonable cost — especially when they include thermal impacts. The lack of models and standards also is slowing the progression of the technology. Still the advantages are worth it to those willing to make the investment. Trad... » read more

Current And Future Packaging Trends


Semiconductor Engineering sat down to discuss IC packaging technology trends and other topics with William Chen, a fellow at ASE; Michael Kelly, vice president of advanced packaging development and integration at Amkor; Richard Otte, president and CEO of Promex, the parent company of QP Technologies; Michael Liu, senior director of global technical marketing at JCET; and Thomas Uhrmann, directo... » read more

Automotive Lidar Technologies Battle It Out


Lidar is likely to be added to the list of sensors that future cars will use to help with navigation and safety, but most likely it won't be the large rotating mirror assembly on the top of vehicles. Newer solid-state radar technologies are being researched and developed, although it’s not yet clear which of these will win. “The benefits of lidar technology are well known dating back to ... » read more

Chipmakers Getting Serious About Integrated Photonics


Integrating photonics into semiconductors is gaining traction, particularly in heterogeneous multi-die packages, as chipmakers search for new ways to overcome power limitations and deal with increasing volumes of data. Power has been a growing concern since the end of Dennard scaling, which happened somewhere around the 90nm node. There are more transistors per mm², and the wires are thinne... » read more

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Power/Performance Bits: Feb. 23


Photonic AI accelerator There are now many processors and accelerators focused on speeding up neural network performance, but researchers at the University of Münster, University of Oxford, Swiss Federal Institute of Technology Lausanne (EPFL), IBM Research Europe, and University of Exeter say AI processing could happen even faster with the use of photonic tensor processors that can handle mu... » read more

Testing Silicon Photonics In Production


As silicon photonics costs come down, the technology is being worked into new applications, from connectivity to AI. But full commercial production requires testing those photonic circuits before shipping them. Photonics testing is only getting started. Volume production is still not happening, and test equipment and techniques are still being developed. What exists today is a blend of exist... » read more

Attaching Fibers To Photonic Chips


Recently, Cadence held its fifth photonics summit, CadenceCONNECT: Photonics Contribution to High-Performance Computing. You can read my earlier posts: Photonic Integration—From Switching to Computing How to Design Photonics If You Don't Have a PhD: iPronics and Ayar Labs The third day was all about how to connect the incoming and outgoing fibers to the photonics chips. I will cov... » read more

Power/Performance Bits: Nov. 17


NVMe controller for research Researchers at the Korea Advanced Institute of Science and Technology (KAIST) developed a non-volatile memory express (NVMe) controller for storage devices and made it freely available to universities and research institutions in a bid to reduce research costs. Poor accessibility of NVMe controller IP is hampering academic and industrial research, the team argue... » read more

Blog Review: Nov. 4


Arm's Joshua Sowerby points to how to improve machine learning performance on mobile devices by using smart pruning to remove convolution filters from a network, reducing its size, complexity, and memory footprint. Mentor's Neil Johnson checks out how designers can write and verify RTL real-time using formal property checking in the style of test-driven development and why to give it a try. ... » read more

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