🤖 AI Summary
In novel sensor development, conventional characterization and parameter optimization heavily rely on expert knowledge and are time-consuming, forming a critical bottleneck. This paper introduces the first closed-loop Bayesian optimization framework specifically designed for sensor characteristic characterization. By integrating real-time measurement feedback with a Gaussian process surrogate model, the method enables fully automated, human-in-the-loop-free exploration of high-dimensional parameter spaces and identification of optimal operating points. It eliminates manual trial-and-error, significantly improving optimization efficiency and reproducibility. Validated on a low-noise CCD sensor, the approach completes full-parameter-space characterization and optimization within two days—accelerating the process by over an order of magnitude compared to conventional methods—while maintaining comparable accuracy. This work establishes a generalizable, automation-first paradigm for intelligent instrument development.
📝 Abstract
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.