A Framework That Generates Chip Layouts Directly From Natural Language Specifications (U. of Bristol, RAL)


A new technical paper, "NL2GDS: LLM-aided interface for Open Source Chip Design," was published by researchers at University of Bristol and Rutherford Appleton Laboratory. Abstract "The growing complexity of hardware design and the widening gap between high-level specifications and register-transfer level (RTL) implementation hinder rapid prototyping and system design. We introduce NL2GDS (... » read more

Follow The AI Leader


In the 1980s, a common expression was "nobody ever got fired for buying IBM." It was considered the safe option, long after new technologies had emerged. While it may not have been the most advanced option available, it remained the safe bet. It had an established ecosystem, and it was a known quantity. But who or what is the safe bet when it comes to AI? Who has the necessary data? Who has ... » read more

Using Data And AI More Effectively In EDA


Key Takeaways The data being produced by EDA tools tends to be for human consumption and has weak semantics. Agents are attempting to create actionable information from unstructured data. The Model Context Protocol may provide AI with access to better data. Semiconductor design generates a lot of data, but how much of that is useful or currently being used by AI tools? And h... » read more

The Race Begins For Much Bigger Abstractions In Data Centers


Key Takeaways Data center build-out is enabling much larger and more complex abstractions. Competition is building for digital/virtual twins across multiple industry segments, including automotive, aerospace, and chip manufacturing. AI, and particularly AI agents, will play a significant role in sorting through data to find potential trouble spots. The frenzy of new data cen... » read more

AI’s Impact On Engineering Jobs May Be Different Than Expected


Key Takeaways: AI is expected to eliminate many repetitive, entry-level tasks, but that may allow engineering students trained on the latest tools to start in more senior positions. AI is a force multiplier. It can accelerate the learning curve for junior engineers. While AI is very good at solving multi-dimensional problems, domain expertise, critical thinking, and sanity checks wil... » read more

Agentic AI In Chip Manufacturing


Agentic AI — breaking AI into individual agents that can work together and collaboratively — will be the real game changer for AI in chip manufacturing. By taking humans out of the loop, these agents can be programmed using natural language to automatically solve problems and improve efficiency. Jon Herlocker, vice president and general manager of software analytics at Cohu, talks  about w... » read more

Revolutionizing Semiconductor Collaboration: The Emergence of AI-Driven Industry Platforms


Demand for advanced computing is robust, driven by AI, cloud technologies, and widespread electrification of the economy. As Moore’s Law slows, the industry is pivoting toward innovative approaches—exploring 3D architectures, chiplets, and sophisticated hybrid packages. Concurrently, the semiconductor landscape is becoming increasingly global, with advanced devices now relying on integratin... » read more

Is End-To-End Security Possible?


Looming financial penalties for data breaches are forcing chipmakers to confront end-to-end security, an increasingly complex and daunting problem because no single company controls all the pieces anymore. This is especially apparent in multi-die assemblies, in use today in data centers, and under consideration in automotive and other applications. Multiple chiplets can push performance well... » read more

Chip Industry’s Top Videos 2025


Rising complexity, new architectures, and AI's permeation of nearly everything left engineers struggling to keep up in 2025, as evidenced by this year's viewership numbers. Among the hottest topics were verification, agentic AI, DRAM/HBM, optimization of data movement, chiplets, and heterogeneous integration, but there was steady traffic growth across all sectors. Top 10 most-watched videos ... » read more

Autonomous ASIC Root Cause Analysis


By Mehir Arora and Zackary Glazewski Over 50% of frontend ASIC hardware engineering time is spent on debugging and root cause analysis, spent churning through millions of lines of code and terabytes of waveform data. Despite this, there are no existing solutions for autonomous root cause analysis that use both code and waveform data. ChipAgents Root Cause Analysis (ChipAgents RCA) is the fir... » read more

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