Review of State-of-the-Art Microwave Filter Tuning Techniques and Implementation of a Novel Tuning Algorithm Using Expert-Based Hybrid Learning

Present-day demand and supply of connectivity necessitate the rapid production of Microwave (MW) filter units. The production of these filters is then followed by the step of utmost importance in the assembly line, viz., the ‘tuning of the filter’, as tuning is crucial to meeting the selectivity requirements of the band. Since the advent of filters, tuning has always been done manually, and hence it is considered a bottleneck by experts in the field. Thus, the need to automate the system is highly implied. The goal of the current work is to outline various MW filter tuning techniques that have been advocated by the community of researchers. The limitations of the said research works and their comparative analysis are also encapsulated in tabular form in the present paper. The paper ends with the implementation of an Expert-Based Hybrid Deep Learning Algorithm to fully automate the filter tuning process.