Accelerate your Digital Transformation with ADS Python Automation

Python Automation Unlocks New Possibilities

This White Paper explores 3 automation Use Cases for expanded Keysight ADS Python APIs transforming modern RF and high-speed design.
 

Use Case 1 – Python for Personal Productivity: One advantage of Python is its easy accessibility to entry-level programmers.  Python is an interpreted language that is free and widely supported on Windows and Linux, is expandable to many domains with plug-ins, and allows immediate corrections for debugging.  In many cases 10 lines of code can replace elaborate procedures using the ADS Graphical User Interface (GUI), saving a lot of time. Even though it is very accessible, Python can also be very powerful and be used in structured ways with formalized tool chains, making it very scalable for workgroups.  Anyone from PA designers to technicians can learn Python quickly and now have a variety of ways of running their code with ADS.
 

User Case 2 – Python for Enterprise Automation: Organizations with larger design teams have additional opportunities for design automation and product lifecycle management.  They use professional software development tools and can afford to invest in custom EDA applications and workflows.  These increase design efficiency, speed the workgroup’s time to market, and formalize IP processes for greater traceability and re-use, plus a host of other functions. They also connect EDA results into formal Product Lifecycle Management (PLM) systems and databases, which interact with enterprise supply chain, manufacturing, compliance, and other functions. 
 

Use Case 3 – Python for Exploring AI/ML: ADS Python automation lays a foundation for implementing the next generation of high-value design tools using AI/ML that promise to transform our industries.
 

Training Data Generation – AI/ML algorithms are dependent on the quality and contents of the training data.  Keysight provides quality, accurate results for both simulated data (Circuit, EM, Thermal, System) and ultimate accuracy using test & measurement data.  When these trusted sources of results are combined with Python automation in ADS, layout and EM parameterization in RFPro, and data reduction techniques with PathWave Data Tools and Keysight ANN modeling, this makes ADS a natural platform to power your AI/ML modeling and application development.
 

Multi-domain Optimization – As noted earlier in the Enterprise Automation section, ADS can be controlled using its APIs to perform many types of simulation, while adjusting either the physical design (layout) or schematic.  This makes external optimization across multiple engineering domains relatively straightforward using PyTorch, TensorFlow, and other tools.  Examples include compacting a working amplified design into a smaller area or reducing thermal hotspots.
 

Pre-Training of Large-Language Models – Chat-based design assistants and “copilots” will soon be able to suggest step-by-step user procedures for many ADS tasks and then, eventually, the API calls needed to execute those steps.  In the meantime, however, commercial Large-Language Model (LLM) agents will not be familiar with your organization’s secure IP.  By creating ADS automation blocks for common actions, organizations can pre-train general-purpose agents to act on their proprietary IP to drive the ADS API to do specific, useful tasks using a natural language interface.  This is just one example of how ADS can accelerate the application of AI/ML in your design organization.