How AI is Revolutionizing Analog and Digital Node Migrations

Sumit Vishwakarma

Aug 14, 2025 / 5 min read

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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Analog node migrations: slow and arduous

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.

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AI: a game changer for analog migration

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:

  • Automatic schematic migration. AI can analyze existing schematics and intelligently adapt them to new process nodes, preserving design intent while accommodating new device characteristics.
  • AI-based circuit optimization. Machine learning algorithms can quickly explore a vast number of variables and optimize analog designs for multiple objectives (like PPA) as well as process-specific constraints.
  • Intelligent layout migration. AI can interpret and adapt complex, handcrafted analog layouts — such as those with proprietary “secret sauce” structures (e.g., finger interlacing in current mirrors) — to new nodes, significantly reducing manual effort.
  • Parasitic-aware optimization: AI-driven tools factor in the impact of parasitics on circuit behavior, ensuring designs remain robust and high-performing across process migrations.
  • Accelerated design closure: Integration with simulation and verification tools (like Synopsys PrimeSim SPICE and IC Validator) allows AI to rapidly iterate and converge on optimal solutions, further speeding up the design cycle.
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Real-world impact: efficiency, quality, and innovation

The use of AI-infused tools for analog node migrations is already delivering tangible benefits:

  • Dramatic time savings. Tasks that used to take weeks or months can now be accomplished in days or even hours, freeing up valuable time and engineering resources.
  • Improved quality and consistency. AI helps standardize migration processes, reducing human error and variability while ensuring best practices are applied consistently.
  • Unlocking new possibilities. By lowering the barrier to migration, AI makes it feasible to port analog designs to more advanced nodes — or even to larger geometries for cost-sensitive applications — expanding the range of products and markets that can be served.
  • Empowering IP teams. Our own IP teams rely on AI-driven tools to migrate and optimize Synopsys analog IP across multiple foundry processes, enabling rapid response to customer needs and market shifts.

Digital node migrations: enhanced by AI, too

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:

  • Automated PPA optimization: AI algorithms can rapidly explore a vast solution space, automatically tuning design parameters to achieve optimal PPA outcomes. This includes adjusting placement, routing, and clock tree synthesis strategies — tasks that previously required significant manual intervention and iterative fine-tuning.
  • Faster design closure: By learning from previous projects and leveraging large datasets, AI tools can anticipate and address common bottlenecks in the flow, such as timing closure and congestion issues. This accelerates convergence and reduces the number of costly design iterations.
  • Seamless IP integration: Integrating new or updated IP blocks is a critical aspect of digital node migration, and we offer a wide variety of IP solutions that are optimized for the latest nodes. AI can intelligently manage compatibility checks, interface adjustments, and performance validation, streamlining the process and minimizing integration risks.
  • Resource optimization: AI can dynamically allocate compute resources and prioritize tasks across distributed design teams, ensuring efficient utilization of engineering talent and EDA infrastructure.
  • Design rule adaptation: As process nodes become more advanced, design rules grow increasingly complex. AI-powered tools can automatically interpret and apply these rules, minimizing the risk of violations and ensuring manufacturability from the outset.
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These AI-driven enhancements offer significant, measurable benefits:

  • Reduced turnaround time: Digital design teams can now transition to new nodes faster, meeting tight delivery windows and accelerating time-to-market for new products.
  • Improved design quality: AI’s ability to optimize across multiple objectives and learn from past projects leads to more robust, higher-quality designs with fewer late-stage surprises.
  • Scalability: AI enables teams to manage the growing size and complexity of system-on-chip (SoC) designs, supporting the migration of everything from small IP blocks to large, multi-billion transistor chips.

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.

The future is AI-driven

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.

 

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