General Principles of Nonlinear Phase Detection for Calibration Optimization in Mass-Production Testing

Tatiana Krasik

Citation: Tatiana Krasik, "General Principles of Nonlinear Phase Detection for Calibration Optimization in Mass-Production Testing", Universal Library of Engineering Technology, Volume 02, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article is devoted to the generalization and systematization of the principles of nonlinear phase detection (NPD) applied to calibration optimization in mass-production testing of diverse products. The relevance of the topic arises from escalating demands for metrological accuracy and test throughput on high-performance production lines, where classical amplitude-based approaches have exhausted their potential. The novelty of this work lies in an interdisciplinary comparison of the ten most recent studies—from deep-network detectors for NPD to in-sample phase calibration in LC-MS. The analysis describes hardware platforms (optical systems, MEMS, DEPFET imagers, Li-ion cells) and algorithmic strategies (DNN, CMA-ES, GA-PSO, U-Net), examines characteristic nonlinearities, and explores methods for their phase-based suppression. Special attention is paid to the scalability of procedures and reduction of systematic error in serial manufacture. The objective is to formulate unified rules for NPD; to this end, comparative, content-analytic, and inductive methods are employed. The conclusion summarizes gains in accuracy and cycle time. This article will benefit metrology engineers, sensor developers, automation specialists, and researchers of optimization algorithms.


Keywords: Nonlinear Phase Detection; Calibration; Mass-Production Testing; Deep Neural Networks; Quantitative Phase Imaging; Evolutionary Algorithms; MEMS Sensors; Li-Ion Batteries; LC-MS; DEPFET Imager.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0202006