Blog review: January 12

Twan Korthorst from Synopsys presents the history of photonics, why it matters to the semiconductor industry, key market applications and the future of photonic integrated circuits.

Paul McLellan of Cadence takes a look at recent TSMC announcements regarding its N3 and N3 HPC ratings and the push for performance gains through co-optimization of design technology

Sebastian Flock from Siemens verifies how virtual drop testing is used to improve the resilience of consumer electronics and the steps involved in creating and running drop test models.

Ansys’ Prith Banerjee highlights a project with Stanford University to investigate how machine learning could be applied to represent geometries in a way suitable for learning partial differential equations used in various Ansys solvers.

Andrea Kells d’Arm reviews research conducted at the Barcelona Supercomputing Center on hardware support for advanced sparse data structures and atomic memory operations, critical instructions used for fine-grained synchronization in multithreaded applications.

SEMI’s Hiroki Yomogita shares highlights from recent SEMICON Japan, including new packaging challenges, the need for new materials, smart factories, and general optimism about the state of the industry.

Richmond Alake from Nvidia gives a crash course on how to identify, read, and get the most out of research papers, with an emphasis on machine learning and data science.

In addition, check out the blogs featured in the latest Automotive, Security & Pervasive Computing and Test, Measurement & Analytics newsletters:

Editor-in-chief Ed Sperling argues that a lot of chip design data across manufacturing is generated, but few people have access to it.

Rahul Singhal of Synopsys discusses why new technologies for AI designs present a significant challenge to the design-for-test process.

Lee Harrison of Siemens EDA examines the security risks of common IC testing strategies and how to mitigate them.

Mike McIntyre of Onto examines how increasingly complex supply chains lead to data traceability issues.

Bart Stevens of Rambus examines the different types of attacks a device can face.

Xilinx’s Ed Rebello demonstrates how to make full use of system resources with the DFX design methodology.

Paul Graykowski of Arteris IP explains how to automatically maintain traceability, from requirements to implementation and verification.

Sam Fuller of Flex Logix explains why it’s so important to match the AI ​​task to the right kind of chip.

Brendan Morris of Siemens analyzes the role of automotive networks in keeping the vehicle running smoothly and protecting the entire system from incorrect sub-system behavior.

Synopsys’ Dana Neustadter examines ways to protect vehicles at the hardware level against a growing number of threats.

Paul McLellan of Cadence focuses on UN efforts to highlight the key component of fiber optics and touch screens.