Explore challenges and solutions in AI chip development
The semiconductor industry has been shaped by the continuous evolution of process technologies. From the early days of planar transistors to the adoption of FinFETs and, most recently, Gate-All-Around (GAA) architectures, each new node brings opportunities for significant improvements in power efficiency, performance, and silicon area (PPA).
This relentless innovation drives companies to migrate their chip designs to newer process nodes — not only to leverage the latest advances in device physics, but also to respond to market demands for smaller, faster, and more energy-efficient products.
And yet, node migration isn’t always about moving to the newest or most advanced process technology. Sometimes, economic considerations — such as cost, yield, and supply chain flexibility — prompt companies to port designs to older process nodes with larger geometries.
Regardless of the overarching goal, migrating a design to a different process technology has historically been a labor-intensive effort fraught with risk — especially for analog designs. This is changing, however, with the help of artificial intelligence (AI).
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While digital design migrations benefit from automation and robust tool flows, analog node migrations have long stood apart as a formidable challenge. Analog circuits are highly sensitive to process variations, and their layouts are often painstakingly handcrafted.
Porting these designs to a new node involves more than simply adapting to new design rules. It demands in-depth understanding of device behavior, meticulous manual adjustments, and, in many cases, complete rework of custom structures.
This highly manual approach makes analog migrations both slow and resource intensive. Even minor changes in process technology can have outsized effects on circuit performance and yield, demanding significant engineering expertise and time commitment.
As a result, analog migrations have traditionally been a bottleneck when adapting semiconductor designs to new process nodes.
AI is now rewriting the rules for analog node migration. Advanced AI-powered tools, such as Synopsys ASO.ai, are streamlining and accelerating the process in ways that were previously unimaginable:
The use of AI-infused tools for analog node migrations is already delivering tangible benefits:
Digital node migrations — which typically involve redoing the full RTL to GDS II flow — are also being transformed by AI. Digital flows have long benefited from automation, but the increasing complexity of modern chips and aggressive PPA targets continue to push the limits of traditional tools and methodologies.
AI-driven solutions, such as Synopsys DSO.ai, are elevating digital node migration to new heights by introducing intelligent automation and optimization at every stage of the flow:
These AI-driven enhancements offer significant, measurable benefits:
In practice, digital teams leveraging AI-powered migration tools report not only faster tapeouts, but also more predictable outcomes and the flexibility to experiment with multiple foundry processes or technology options.
AI is rapidly transforming node migration from a painful necessity into a strategic enabler of innovation in semiconductor design — most notably in the analog domain, where the impact is greatest. By automating complex, manual tasks, AI allows teams to migrate designs with unprecedented speed, reliability, and creativity. As the industry continues to evolve, the synergy between AI and human expertise will be the key to unlocking the full potential of next-generation semiconductor technologies.