Inverse Design of Near-Field Transducer for Heat-Assisted Magnetic Recording Using Topology Optimization

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/in
²
. 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.