New Ways To Improve EDA Productivity


EDA vendors are taking aim at new ways to improve the productivity of design and verification engineers, who are struggling to keep pace with exponential increases in chip complexity in tight time-to-market windows and with constrained engineering talent pipelines. In the past, progress often was as straightforward as improving algorithms or parallelizing computations in a linear flow. But w... » read more

AI For The Edge: Why Open-Weight Models Matter


The rapid advancements in AI have brought powerful large language models (LLMs) to the forefront. However, most high-performing models are massive, compute-heavy, and require cloud-based inference, making them impractical for edge computing. The recent release of DeepSeek-R1 is an early, but unlikely to be the only, example of how open-weight AI models, combined with efficient distillation t... » read more

The Rise Of Generative AI On The Edge


Artificial intelligence (AI) and machine learning (ML) have undergone significant transformations over the past decade. The revolution of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) is evolving toward the adoption of transformers and generative AI (GenAI), marking a pivotal shift in the field. This transition is driven by the need for more accurate, efficient, and ... » read more

First-Time Silicon Success Plummets


First-time silicon success is falling sharply due to rising complexity, the need for more iterations as chipmakers shift from monolithic chips to multi-die assemblies, and an increasing amount of customization that makes design and verification more time-consuming. Details from a new functional verification survey[1] highlight the growing difficulty of developing advanced chips that are both... » read more

What Scares Chip Engineers About Generative AI


Experts At The Table: LLMs and other generative AI programs are a long way away from being able to design entire chips on their own from scratch, but the emergence of the tech has still raised some genuine concerns. Semiconductor Engineering sat down with a panel of experts, which included Rod Metcalfe, product management group director at Cadence; Syrus Ziai, vice-president of engineering at E... » read more

Automotive Outlook 2025: Ecosystem Pivots Around SDV


The automotive industry is deep in the throes of a massive shift to software-defined vehicle architectures, a multi-year effort that will change the way automotive chips are designed, where they are used, and how they are sourced. Creating a new vehicle architecture is no small feat. OEMs need to figure out who to partner with and which aspects of their current architecture to include. This ... » read more

2025: So Many Possibilities


The stage is set for a year of innovation in the chip industry, unlike anything seen for decades, but what makes this period of advancement truly unique is the need to focus on physics and real design skills. Planar scaling of SoCs enabled design and verification tools and methodologies to mature on a relatively linear path, but the last few years have created an environment for more radical... » read more

AI Won’t Replace Subject Matter Experts


Experts at The Table: The emergence of LLMs and other forms of AI has sent ripples through a number of industries, raising fears that many jobs could be on the chopping block, to be replaced by automation. Whether that’s the case in semiconductors, where machine learning has become an integral part of the design process, remains to be seen. Semiconductor Engineering sat down with a panel of e... » read more

Research Bits: Jan. 7


Deep UV microLED for maskless lithography Researchers from the Hong Kong University of Science and Technology, Southern University of Science and Technology, and the Suzhou Institute of Nanotechnology developed an aluminum gallium nitride deep-ultraviolet microLED display array for maskless lithography.  They also built a maskless lithography prototype platform. "The team achieved key brea... » read more

The Future Of Technology: Generative AI In China


China's investment in Gen-AI is projected to surge with an estimated 86% CAGR over the next five years. This growth is driven by a focus on technological self-sufficiency, from applications to chips, and a strong emphasis on locally developed technology. Key areas of development include: AI Chip Development: Addressing the need for powerful AI infrastructure. Dataset Localization: S... » read more

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