Publication:
Machine Learning Assists the Design of a W-Band Phase-Change Tunable Wire-Grid Periodic Resonant Broadband Absorber
Date
2026
Journal article
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Author(s)
Journal
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
Abstract
Millimeter-wave systems require absorbers with high absorption efficiency, broad bandwidth, and multi-functional tunability. However, existing devices based on composite structures and materials face design challenges: traditional methods hardly achieve all three indicators simultaneously, often sacrificing one performance for another, making “high absorption, broad bandwidth, multi-function” integration highly difficult. To overcome this fundamental trade-off, this study designs a W-band (75–110 GHz) composite absorber, integrating VO2 and graphene, and combining periodic resonant structures with a backplane-free phase-change wire-grid architecture. When integrated into the periodic resonant structure, the VO2 wire-grid provides thermal ‘on-off’ switching of absorption, whereas graphene offers electrical tunability—both achieved by modulating the effective surface conductivity and thus the impedance matching condition. Guided by an XGBoost-based closed-loop inverse design framework, the XGBoost model optimizes parameters, with convergence achieved after four closed-loop simulation iterations. Results show the VO2 wire-grid absorber performs excellently at 90° incidence: it achieves near-perfect absorption (>99%) over a 21 GHz sub-band (82–103 GHz), showing an 80% improvement in average absorption compared to copper(Cu) backplanes, under identical unit-cell geometry and simulation conditions, with each configuration independently optimized for peak performance. Graphene provides a maximum modulation amplitude of depth of 68%. Innovations of this study include constructing a periodic resonant wire-grid structure that abandons traditional backplanes and uses wire-grids to expand loss areas, enabling dual-mode multi-physics control through co-integration of VO2 and graphene, and establishing a machine learning closed-loop framework for parameter optimization. It explores a new paradigm for multi-functional tunable absorbers, providing high-performance solutions for MMW systems such as radar, communications, and biomedical imaging.