Designing For Reliability With A System Life Estimator


From big machines to small handheld equipment, all typically come with varying warranty timelines based on estimations. If this number is overestimated or underestimated, it can incur millions of dollars in losses to manufacturers. That’s why it’s important to look at the lifetime system estimation as a bottom-up process. The efficiency in this approach results in a robust method to formula... » read more

Chip Industry Week in Review


Qualcomm announced plans to buy Alphawave Semi for ~$2.4 billion in a deal expected to close in Q1 2026. Qualcomm plans to leverage Alphawave Semi's connectivity products, including chiplets, to develop high-performance, low-power solutions for AI inferencing and customized CPUs in data centers. Qualcomm's traditional targets were mobile phones and edge computing. [Updated 6/9.] Global semic... » read more

How Secure Are Analog Circuits?


The move toward multi-die assemblies and the increasing value of sensor data at the edge are beginning to focus attention and raise questions about security in analog circuits. In most SoC designs today, security is almost entirely a digital concern. Security requirements in digital circuits are well understood, particularly in large data centers and at the upper end of edge computing, which... » read more

Accelerating Chiplet-Based SoC Design For AI-Defined Vehicles


Today, we are witnessing a paradigm shift towards a more modular design approach that disaggregates SoC functions into various chiplets, each optimized for specific functionalities. Chiplets offer more modularity, which enables product scalability and customization, which is key for AI-defined vehicles, the next generation of software-defined vehicles (SDVs). Cadence’s Helium Virtual and H... » read more

Mobile Chip Challenges In The AI Era


Leading smart phone vendors are struggling to keep pace with the rising compute and power demands of localized generative AI, standard phone functions, and the need to move more data back and forth between handsets and the cloud. In addition to edge functions, such as facial recognition and other on-device apps, phones must accommodate a continuous stream of new communications protocols, and... » read more

AR/VR Glasses Taking Shape With New Chips


More augmented reality (AR), virtual reality (VR), and mixed reality (MR) wearables are coming, but how they are connected, and where image and other data is processed, are still in flux. Ray-Ban Meta AI glasses, for example, look like classic eyeglasses, but they rely on a tethered smart phone for such functions as taking pictures, AI voice assistance, and object identification. In contrast... » read more

A New RF Platform For Silicon MMIC And System Design


Next generation wireless modules combine innovative and proven RFIC/MMIC designs into a single package―delivering superior performance, lower power consumption, and reduced size, weight, and cost. Advanced system engineering plays a crucial role in this progress, with a strong focus on packaging interconnect design, RF analysis, and thermal and electromagnetic (EM) awareness that enable se... » read more

Blog Review: June 4


In a podcast, Siemens’ Conor Peick, Dale Tutt, and Mike Ellow chat about the implications of the software-defined transition, how it affects semiconductor development, and why it seems to be leading more companies towards developing their own silicon. Cadence’s Vinod Khera shows off a Linux-based audio development platform for prototyping AI audio applications with support for real-time ... » read more

Connecting AI Accelerators


Experts At The Table: Semiconductor Engineering sat down to discuss the various ways that AI accelerators are being applied today with Marc Meunier, director of ecosystem development at Arm; Jason Lawley, director of product marketing for AI IP at Cadence; Paul Karazuba, vice president of marketing at Expedera; Alexander Petr, senior director at Keysight; Steve Roddy, chief marketing office... » read more

Boosting AI Performance With CXL


As AI applications rapidly advance, AI models are being tasked with processing massive amounts of data containing billions – or even trillions – of parameters. Each large workload involves numerous iterations for data comparison, predictive calculations, and parameter results updating during training. Hence, there is a constant demand for flexible memory expansion and memory sharing among d... » read more

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