Human Language to Analog Layout Using Glayout Layout Automation

Current approaches to Analog Layout Automation
apply ML techniques such as Graph Convolutional Neural
Networks (GCN) to translate netlist to layout. While these ML
approaches have proven to be effective, they lack the powerful
reasoning capabilities, an intuitive human interface, and standard
evaluation benchmarks that have been improving at a rapid de-
velopment pace in Large Language Models (LLMs). The GLayout
framework introduced in this work translates analog layout into
an expressive, technology generic, compact text representation.
Then, an LLM is taught to understand analog layout through
fine-tuning and in-context learning using Retrieval Augmented
Generation (RAG). The LLM is able to successfully layout unseen
circuits based on new information provided in-context. We train
3.8, 7, and 22 Billion parameter quantized LLMs on a dataset
of less than 50 unique circuits, and text documents providing
layout knowledge. The 22B parameter model is tuned in 2 hours
on a single NVIDIA A100 GPU. The open-source evaluation
set is proposed as an automation benchmark for LLM layout
automation tasks, and ranges from 2-transistor circuits to a
∆Σ ADC. The 22B model completes 70% of the tasks in the
evaluation set, and is able to pass DRC and LVS verification on
unseen 4 transistor blocks.