All-digital high-performance microwave reflectometry
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
Highly sensitive microwave (MW) reflectometry, like those used in Microwave Impedance Microscopy for probing local electronic properties in solids (see e.g. https://science.sciencemag.org/content/350/6260/538), have been built with bulky, expensive, highly specialized, mostly manually controlled components so far. This project aims to explore the possibility of using all-digital, modular, cheaper, but still high-performance components made available by the wifi and cellular industry in recent years.
Role: Working directly with me and in collaboration with another URAP student who did the microwave system design last semester, you will build and validate this all-digital microwave reflectometer. You will start by learning and understanding the microwave system design, before designing and building a DC-300 kHz ultrasensitive voltage amplifier for the low-frequency end of the instrument. You will then learn to assemble modular surface-mount-based microwave IC blocks (https://www.xmicrowave.com/) and test all the digital I/O via a Raspberry Pi + TCP/IP interface to Python running on a PC. You will design the power-up/down sequences, automatic offset removal (with traditional methods or machine learning), and other algorithms necessary for the operation of the system, before finally validating the system's performance and, if time permits, benchmarking with a very expensive state-of-art commercial system.
Join our team and be part of an exciting journey of building a new modern optoelectronics lab (https://sites.google.com/berkeley.edu/ma-lab) from scratch.
Qualifications: Required: a team player, motivation to spend the time in the lab and learn new skills, experience in designing and building RF/microwave and sensitive low-f analog circuits, experience in working with microcontrollers and related digital I/O protocols like SPI, proficiency in Python.
Good to have: experience in optimization algorithms/machine learning.
Hours: 9-11 hrs
Related website: https://sites.google.com/berkeley.edu/ma-lab
Related website: https://www.xmicrowave.com/