R&D: Inverse Design of Near-Field Transducer for HAMR Using Topology Optimization

IEEE Transactions on Magnetics has published an article written by Prabhu K. Venuthurumilli, Zhou Zeng, and Xianfan Xu, School of Mechanical Engineering, Birck Nanotechnology Center, Purdue University, West Lafayette, IN, USA.

Abstract: “Heat assisted magnetic recording (HAMR) is one of the next generation technologies in data storage that can increase the areal density to beyond 1.5 Tb/in². Near-field transducer (NFT) is a key component of the HAMR system that locally heats the recording medium by concentrating light below the diffraction limit using surface plasmons. In this work, we use density-based topology optimization for inverse design of NFT for a desired temperature profile in the recording medium. We first perform an inverse thermal calculation to obtain the required volumetric heat generation (electric field) for a desired temperature profile. Then an inverse electromagnetic design of NFT is performed for achieving the desired electric field. NFT designs for both generating a small heated spot size and a heated spot with desired aspect ratio in recording medium are demonstrated.“