Blog Review: Apr. 22


In a podcast, Siemens EDA's Harry Foster and Vladislav Palfy chat about why coverage closure has become one of the biggest bottlenecks in modern verification and how a unified approach that combines planning, automation, and analytics helps teams break through coverage plateaus. Synopsys' Emily Gerken and Marc Swinnen consider the challenges of designing analog and mixed-signal circuits at a... » read more

Why Proof Convergence Matters


Achieving a deterministic "yes or no" answer in semiconductor verification is becoming more challenging as chip complexity increases. There are more cores, more potential interactions, and more reliance on AI to build AI chips. Ashish Darbari, CEO of Axiomise, talks about the impact of functional interactions involving safety and security, and where to look for common patterns to prevent bugs f... » read more

Blog Review: Apr. 15


Cadence's Wilson Kobalkar shares why eUSB2‑V2 represents a major evolutionary step for the USB 2.0 ecosystem, including how it achieves multi‑gigabit HSx operation and why symmetric/asymmetric modes unlock new design possibilities. Synopsys' Akanksha Soni explains the difference between metal-oxide-metal, metal-insulator-metal, and metal-oxide-semiconductor capacitors, identifying the ad... » read more

AI Growing Impact On Chip Design And EDA Tools


Key Takeaways Many workflows in the data center are customer-specific, which is part of the reason there is so much interest in agentic AI-enabled tools. Large systems companies are pressing EDA vendors for performance improvements to keep pace with their AI workflows. The makeup of design teams is changing as AI infiltrates more of the chip design process. Experts at the Ta... » read more

Startup Funding: Q1 2026


The new year started off with a bang for private semiconductor companies, with 18 garnering mega funding rounds exceeding $100 million, and two, Rapidus and Cerebras, reaching the $1 billion mark. Predictably, the vast majority of those are either designing chips primarily for AI inference workloads or attempting to overcome bandwidth limitations by improving interconnects from the chip level t... » read more

Blog Review: Apr. 8


Cadence's Shyam Sharma highlights new capabilities in LPDDR6, including metadata built into the data packets, rowhammer mitigations, DVFS with support for three operating voltage rails, and new efficiency modes. Synopsys' Akanksha Soni points to multiphysics simulation as a key element of ensuring automotive IC designs meet ISO 26262 requirements. Siemens' John McMillan suggests a simulat... » read more

Blog Review: April 1


Siemens EDA's Harry Foster considers why first-silicon success is continuing to decline even though tools are capable of handling much larger design sizes and identifies how increasingly complex interactions between components cause traditional verification assumptions to break down. Synopsys' Eldo N Baby explores dynamic voltage drop analysis, including how to bring in switching scenario in... » read more

AI’s Potential And Limitations In Chip Design


Experts at the Table: Semiconductor Engineering sat down to discuss the opportunities and challenges of using AI in chip design, with Thomas Andersen, vice president for AI & Machine Learning at Synopsys; Sridhar Boinapally, senior director of analog/mixed signal tools/flow at Intel; Alex Starr, corporate fellow at AMD; Stuart Oberman, vice president for GPU hardware engineering at Nvidia; ... » read more

All Software Is Hardware-Dependent


I was lucky in my early career that I found two sets of great mentors. The first happened recently after graduating when I joined the Hilo development team. Members of that team included Phil Moorby, Simon Davdimann, Peter Flake, and others. They all had very different coding personalities, but most importantly, they worked as a team and used good foundational processes. One outcome of that ... » read more

AI Workloads Are Turning The Data Center Network Into A Combined Memory And Storage Fabric


Recent industry trends, including the release of NVIDIA’s Rubin platform (developer.nvidia.com), point to a growing consensus that AI inference is reshaping data center architecture in a fundamental way. As inference workloads become dominant, the data center network is no longer just a communication layer between servers. It is increasingly part of a distributed memory and storage hierarchy,... » read more

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