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

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.