Low-Temperature AFM Instrumentation
Cryogenic scanning probe workflows for silicon nanodevices (quantum-device-adjacent measurement)
Problem
Low-temperature scanning probe experiments are unforgiving: you’re balancing mechanical stability, thermal constraints, low-noise wiring, and measurement throughput, all while making sure the data is interpretable and repeatable. The goal here was not “one-off results,” but a system and workflow that behaves predictably and can be extended.
What I worked on
- Instrument integration: coordinated cryogenic operation, mechanical constraints, electrical feedthroughs, and low-noise measurement requirements into a single practical workflow.
- Mechanical design: CAD modelling, prototyping via 3D printing, producing precision engineering drawings, and recquisitioning custom parts to upgrade the mechanics of the microscope to increase efficiency, reliability, and productivity.
- Electrical wiring: Rewired (almost) the entire microscope to improve strain relief and signal quality, including soldering dozens of wires down to 34 AWG.
- DAQ + control: built and refined data acquisition/control routines to support stable measurement runs (automation where it improves reliability, not just convenience).
- Signal quality and reliability: troubleshooting and iteration focused on reproducible signal quality, drift/stability awareness, and “why did this fail?” diagnostics.
- Analysis pipelines: Python-based analysis and visualization used to sanity-check measurements quickly and to make iteration cycles faster.
- Documentation: prioritized notes and procedures that make the setup maintainable and usable by others (the difference between “it works” and “it’s a system”).
System overview
Architecture (high level)
- Environment: cryogenic operation (low-temperature constraints drive everything)
- Probe + mechanics: stability and vibration awareness; careful mechanical choices
- Electronics: low-noise wiring/grounding mindset; avoid “mystery coupling”
- Acquisition: DAQ/configuration + scripted routines for repeatability
- Analysis: fast feedback loop via Python (plots that catch bad runs early)
On purpose, this page stays at a “systems engineering” level rather than listing sensitive lab details. The key message is how the work was approached: stability, noise-awareness, automation for reliability, and analysis-driven iteration.
Technical skills demonstrated
Engineering
Computation
Why this matters (industry framing)
This work is fundamentally about building measurement systems that other people can trust. That translates directly to industry R&D: integrating hardware and software, managing constraints, debugging real systems, and delivering workflows that are repeatable, documented, and maintainable.