AI-Assisted Automation for Advanced Packaging

Complex advanced-package layouts can require significant engineering effort, particularly when designs must accommodate detailed physical constraints, implementation rules, and frequent revisions. As packaging complexity increases, manually repeating these processes can extend development cycles and make it difficult to consistently reuse proven engineering methodologies across projects.

This application note demonstrates how a Large Language Model (LLM) can be combined with ADS Python automation to accelerate the implementation of a complex UCIe advanced-package layout. The methodology provides the LLM with design requirements, physical constraints, engineering rules, and relevant ADS Python API references. Through iterative refinement, this information is transformed into executable, parameterized automation scripts capable of generating the required package layout.

The case study highlights substantial improvements in implementation and iteration time. Using a conventional manual approach, the initial layout would typically require more than a week of engineering effort, with subsequent revisions taking approximately three additional days. With AI-assisted automation, the initial implementation was reduced to about one day. Later revisions could be completed in roughly 30 minutes by updating design parameters and regenerating the layout.

Beyond these immediate productivity improvements, the approach captures the underlying implementation methodology in reusable automation. Instead of recreating layouts manually for each new project, engineers can adapt proven scripts to different package configurations, helping improve consistency while reducing repetitive engineering work. In the demonstrated workflow, the original automation was subsequently reused to implement another package configuration in only a few hours.

Although the application note focuses on UCIe advanced packaging, the methodology can extend to other ADS workflows, including schematic generation, design verification, and evaluation. Tasks involving repetitive operations, complex engineering rules, and frequent iterations are particularly suited to this approach.

As libraries of reusable automation expand, they can provide a practical foundation for increasingly connected and intelligent ADS workflows, ultimately supporting agent-driven engineering processes capable of translating higher-level design objectives into complex engineering tasks.