Future of photonics; TSMC’s N3 and DTCO; ML for physics solvers; drop tests.
Synopsys’ Twan Korthorst introduces the history of photonics, why it is important for the semiconductor industry, key market applications, and the future of photonic integrated circuits.
Cadence’s Paul McLellan takes a look at TSMC’s recent announcements around its N3 and N3 HPC notes and the push for performance gains through design technology co-optimization
Siemens’ Sebastian Flock checks out how virtual drop tests are used to improve the resiliency of consumer electronics and the steps involved in creating and executing drop test models.
Ansys’ Prith Banerjee highlights a project with Stanford University to investigate how machine learning might be applied to represent geometries in a manner that is suitable for learning partial differential equations used in various Ansys solvers.
Arm’s Andrea Kells checks out research happening at the Barcelona Supercomputing Center on hardware support for advanced sparse data structures and on Atomic Memory Operations, critical instructions that are used for fine grain synchronization in multithreaded applications.
SEMI’s Hiroki Yomogita shares highlights from the recent SEMICON Japan, including new packaging challenges, the need for new materials, smart factories, and overall optimism about the state of the industry.
Nvidia’s Richmond Alake gives a crash course in how to identify, read, and get the most out of research papers, with a focus on machine learning and data science.
Editor in Chief Ed Sperling contends that plenty of chip design-through-manufacturing data is being generated, but not enough people have access to it.
Synopsys’ Rahul Singhal analyzes why new technologies for AI designs present a significant challenge to the design-for-test process.
Siemens EDA’s Lee Harrison examines the security risks of common IC test strategies and how to mitigate them.
Onto’s Mike McIntyre looks at how increasingly complex supply chains lead to data traceability challenges.
Rambus’ Bart Stevens looks at the different types of attacks a device might face.
Xilinx’s Ed Rebello shows how to fully utilize system resources with the DFX design methodology.
Arteris IP’s Paul Graykowski outlines how to automatically maintain traceability, from requirements to implementation and verification.
Flex Logix’s Sam Fuller explains why it’s so important to match the AI task to the right type of chip.
Siemens’ Brendan Morris dissects the role of automotive networks in ensuring correct vehicle functionality and protecting the entire system from incorrect sub-system behavior.
Synopsys’ Dana Neustadter examines ways to protect vehicles at the hardware level from an increasing number of threats.
Cadence’s Paul McLellan zeroes in on the UN’s efforts to highlight the key component of fiber-optics and touchscreens.
Jesse Allen is the Knowledge Center administrator and a senior editor at Semiconductor Engineering.
- Consumer electronics
- Data Science
- How To
- machine learning
- Stanford University
- Supply chains
- The Future