Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
This work addresses the need for rapid or automated design of RF/mm-Wave circuits, but it appears incremental as it applies an existing neural network approach to a specific domain.
The authors tackled the problem of automating the design of on-chip electromagnetic structures by proposing a machine learning model for direct synthesis, demonstrating it on a 1:1 transformer in a 45nm SOI process to predict geometric designs from s-parameter files.
We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits. As a proof of concept, we demonstrate the direct synthesis of a 1:1 transformer on a 45nm SOI process using our proposed neural network model. Using pre-existing transformer s-parameter files and their geometric design training samples, the model predicts target geometric designs.