# Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

arXiv: https://arxiv.org/abs/2008.10755 · HTML: https://mlsanity.com/paper/2008.10755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [MOTIF-RF: Multi-template On-chip Transformer Synthesis Incorporating Frequency-domain Self-transfer Learning for RFIC Design Automation](https://mlsanity.com/paper/2511.21970/) (2025)
- [Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network](https://mlsanity.com/paper/2201.02141/) (2022)

## Abstract (arXiv)

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.