Automation

I've built several probe stations used for electronic and magnetic measurements.

• MRAM ns-pulse switching measurement station (picture)

• High-frequency spin-torque ferrormagnetic resonance station

• Low-noise cryogenic transport measurement station for superconducting circuits and photon detectors

I've developed several Python packages (pictures) to communicate with electronic instruments and automate the measurement processes so that one can quickly build a centralized Python program to operate the instruments and collect data automatically.

Instrumentation with a powerful programming language like Python can incorporate data processing and ML/AI into measurements, thus enables complex measurement procedures and efficient recursive steps (see below).

When instrumentation, measurements and data analyses are carried out in the same platform (in this case, Python), learnings from the previous measurement can be fed directly to the next one to improve efficiency.

In this example (PR Applied), we want to find the switching boundary on the phase diagram. The adaptive algorithm (Adapt) finds the locations on the phase diagram that are in the proximity of the boundary (high gradient) and feeds them to the next measurement. This adaptive strategy resulted in probably the highest resolution phase diagrams in the MRAM industry.