Wideband Sandwich Metamaterial Window With Thick BN for W-Band gyro-TWT

Abstract

This letter proposes a sandwich metamaterial window (SMW) for a W-band TE01 mode gyrotron travelling-wave tube (gyro-TWT), which is optimized using machine learning (ML) to achieve high transmissivity and low reflectivity in a wideband range. This SMW has three layers, BN-metal-BN, where the BN has a thickness of 1.05 mm. The metal layer is composed of hexagonal units allowing to improve the cold-test characterization of SMW. The ML optimization makes the SMW have a high simulation performance with S11 less than −20 dB and S21 higher than −0.14 dB within 12.15 GHz. Compared to conventional designs, the proposed SMW provides a thicker one to achieve comparable performance through a simplified fabrication process. Experimental validation confirms alignment with simulation results, demonstrating the feasibility of this approach. This work provides a scalable and cost-efficient methodology for high-frequency metasurface window design.