Automating Sensor Characterization with Bayesian Optimization
This work addresses a time-consuming problem for researchers and engineers in instrumentation development, offering a significant acceleration in testing phases.
The paper tackles the bottleneck of sensor characterization in device development by introducing an automated calibration technique using Bayesian optimization, demonstrating it can reduce characterization time from over a year to a couple of days for a low-noise CCD.
The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.