Space Imaging Sensor Power Supply Filtering: Improving EMC Margin Assessment with Clustering and Sensitivity Analyses

• Published in: Electronics (Basel) Vol. 10; no. 18; p. 2301
• Main Authors: Patier, Laurent, Lalléchère, Sébastien
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2021
• Subjects: Clustering; Electric components; Electric power supplies; Electromagnetic compatibility; Filtration; Integrated circuits; Mathematical analysis; Mercury cadmium telluride; Optoelectronic devices; Parameters; Power supply; Sensitivity analysis; Sensors; Space applications; Subsystems; Tolerances; Topology; Transfer matrices; Transistors
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10182301

This work is dedicated to the assessment of the filtering performances of an optoelectronic sensor for space applications. Particular care is taken concerning the power supply subsystem (here voltage shifter integrated circuit), which is part of the electromagnetic compatibility (EMC) compliance of an imaging equipment embedded on spacecrafts. The proposed methodology aims at two major targets: First, evaluating the Filter Effectiveness (FE) subject concerning varying parameters (including filter topology, parasitic effects and source/load impedance variations); second, quantifying the relative importance of representative equivalent electrical components through sensitivity analyses (nominal and parasitic values). The latter point is of utmost importance considering the expected versatility of such systems, such as manufacturing tolerances, for instance. Nominal values and/or components are often badly defined for confidentiality reasons, lack of knowledge or pure ignorance of inputs. An analytical deterministic formulation (here through the transfer matrix approach) is proposed and completed with an original stochastic strategy (Reduced Order Clustering, ROC). This ensures the reliable assessment of both statistical filter performances and most influential parameters, jointly with computational resources saving relatively to brute force Monte Carlo simulations.