Author's Latest Posts


System-in-Package Challenges


Systems companies and leading-edge chipmakers are pushing past reticle limits with chiplet-based designs, often breaking compute-intensive functions into different chiplets and coupling those with other chiplets that may have been developed by different teams and at different process nodes. This is harder than it sounds, and results can vary widely even under the best circumstances. Nir Sever, ... » 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

Batteries Charge To The Edge


Long-awaited advances in battery chemistry and materials science are beginning to roll out, opening the door for higher capacity, faster charging, and much lower likelihood of thermal runaway. This is a high-stakes race, fueled by an insatiable demand for power everywhere from handheld devices to data centers. When Finland's Donut Lab claimed earlier this year that it had developed a solid-s... » read more

Why More CPUs Are Needed For Agentic AI


The shift from generative AI to agentic AI will significantly increase the amount of compute power needed in data centers. Queries to search for and analyze data from multiple sources will be performed simultaneously by agents and without human intervention, rather than a single request from a live person. Jeff Defilippi, senior director of product management at Arm, talks about the impact of r... » 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

EDA And IP Numbers Up Again, But Numbers Are More Nuanced


EDA and Semiconductor IP revenue grew 10.3% in Q4 2025 to $5.466 billion, up from $4.955 billion in the same period in 2024, continuing the double-digit run for the tools and IP business that has been underway for the past few years. CAE, the largest EDA category, rose 9.4% to $2.083 billion in Q4, versus $1.761 billion in Q4 2024. Non-reporting IP companies — a segment dominated by Arm �... » read more

State Of The Market For Edge Silicon


The explosion of data and the rapid ramp of AI is causing significant changes in how chips are architected. At the edge, the key metrics are power, latency, and performance, but those can vary significantly by application and by workload. Steve Roddy, chief marketing officer at Quadric, talks about the need to balance performance and efficiency with flexibility for different applications, what ... » 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

Challenges In Scaling Chips To 2nm And Below


Key Takeaways Scaling to 2nm and below continues due to power improvements per watt, but progress is much more challenging and costly. Solutions to problems often create other problems due to less margin for tradeoffs, often requiring larger interposers, more chiplets, and more complex packages. New levels of precision are required throughout the design-through-manufacturing flow, re... » read more

Memory For AI At The Edge


Inferencing at the edge has very different needs than training large language models or large-scale inferencing in AI data centers. Many edge devices run on a battery. They're price-sensitive, and they are constrained by the physical area of the device. As a result, the amount of memory that can be packed into these devices is also limited. Steve Woo, Rambus fellow and distinguished inventor, t... » read more

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