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Custom and Analog-Mixed Signal (AMS) IC design are used when the highest performance is required, and using digital standard cells just won’t meet the requirements. Manually sizing schematics, doing IC layout, extracting parasitics, then measuring the performance only to go back and continue iterating is a long, tedious approach. Siemens EDA has been offering EDA tools that span a wide gamut, including: High Level Synthesis, IC design, IC verification, physical design, physical verification, manufacturing and test, packaging, electronic systems design, electronic systems verification and electronic systems manufacturing. Zooming into the categories of IC design and IC verification is where tools for Custom IC come into focus, like the Solido Design Environment. I had a video conference with Wei Tan, Principal Product Manager for Solido to get an update on how AI is being used.
Designing an SoC at 7nm can cost up to $300 Million, and 5nm can reach $500 Million, so having a solid design and verification methodology is critical to the financial budget, and the goal of first pass silicon success. With each smaller process node the number of PVT corners required for verification only goes up.
The general promise of applying AI to the IC design and verification process is to improve or reduce the number of brute-force calculations, assist engineers to be more productive, and to help pinpoint root causes for issues like yield loss. Critical elements of using AI in EDA tools include:
Verifiability- the answers are correct
Usability -non-experts can use the tools without a PhD in statistics
Generality – it works on custom IC, AMS, memory and standard cells
Robustness – all corner cases work properly
Accuracy – same answers as brute-force methods
Wei talked about three levels of AI, with the first being Adaptive AI which accelerates an existing process using AI techniques, the next level as Additive AI that retains previous model answers in new runs, and the final level of Assistive AI to help circuit designers be more productive with new insights while using generative AI.
Solido has some 15 years of applying AI techniques to EDA tools used by circuit designers at the transistor level. For Monte Carlo simulations using Adaptive AI there’s up to a 10,000X speedup so you can get 3 to 6+ sigma results at all corners that matches brute-force accuracy. Here’s an example of Adaptive AI where a 7.1 sigma verification that required 10 trillion brute-force simulations only used 4,000 simulations, or 2,500,000,00X faster with SPICE accuracy.
The Solido Design Environment also scales well in the cloud to speed up simulation runs using AWS or Azure vendors to meet peak demands.
An example of Additive Learning employs AI model reuse for when there are multiple PDK revisions and you want to characterize your entire standard cell library for each new PDK version. The traditional approach would require 600 hours to do the initial PVT runs using Monte Carlo, covering five revisions.
With AI model reuse this scenario takes much less time to complete, also saving many MB to GB of data saved on disk.
Assistive AI is applied to the sizing of transistors and identifies optimization paths to improve PPA, determines the optimal sizing of transistors to achieve the target PPA goals, and has friendly reports to visual the progress. You can expect your IC team to save days to weeks of engineering time by using AI-assisted optimization.
Custom and AMS IC designers can now apply AI-based techniques in their EDA tool flows during both design and verification stages. Adaptive AI speeds up brute-force Monte Carlo simulation, Additive learning uses retained AI models to speed up runs, and Assistive AI is applied to circuit optimization and analysis.
Yes, you still need circuit designers to envision transistor-level circuits, but they won’t have to wait so long for results when using EDA tools that have AI techniques under the hood.