Machine Learning-Assisted On-chip Quadrilateral Interleaved Transformer Automatic Synthesis
- Resource Type
- Conference
- Authors
- Wei, Yawen; Wei, Jiahao; Wu, Qi; Wang, Haiming
- Source
- 2023 16th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies (UCMMT) Millimetre Waves and Terahertz Technologies (UCMMT), 2023 16th UK-Europe-China Workshop on. 1:1-3 Aug, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Radio frequency
Machine learning algorithms
Millimeter wave circuits
Computational modeling
Optimization methods
Terahertz materials
Transformers
Machine learning
transformers
automatic synthesis
- Language
- ISSN
- 2639-4537
The design of millimeter-wave on-chip passive transformers, which is an important component of radio-frequency (RF) circuits, has always depended on heavily computational full-wave electromagnetic (EM) simulations and specific design experience. A machine learning-assisted on-chip transformer automatic synthesis (OTAS) algorithm is proposed. This algorithm is applied to the quadrilateral transformer synthesis and is verified by designing a symmetrical quadrilateral interleaved transformer at 50 GHz to maximize available gain. OTAS can greatly reduce full-wave EM simulations while improving the performance of on-chip transformers.