Applied surface analysis in magnetic storage technology

Abstract

This paper gives a synopsis of today’s challenges and requirements for a surface analysis and materials science laboratory with a special focus on magnetic recording technology. The critical magnetic recording components, i.e. the protective carbon overcoat (COC), the disk layer structure, the read/write head including the giant-magnetoresistive (GMR) sensor, are described and options for their characterization with specific surface and structure analysis techniques are given. For COC investigations, applications of Raman spectroscopy to the structural analysis and determination of thickness, hydrogen and nitrogen content are discussed. Hardness measurements by atomic force microscopy (AFM) scratching techniques are presented. Surface adsorption phenomena on disk substrates or finished disks are characterized by contact angle analysis or so-called piezo-electric mass adsorption systems (PEMAS), also known as quartz crystal microbalance (QCM). A quickly growing field of applications is listed for various X-ray analysis techniques, such as disk magnetic layer texture analysis for X-ray diffraction, compositional characterization via X-ray fluorescence, compositional analysis with high lateral resolution via electron microprobe analysis. X-ray reflectometry (XRR) has become a standard method for the absolute measurement of individual layer thicknesses contained in multi-layer stacks and thus, is the successor of ellipsometry for this application. Due to the ongoing reduction of critical feature sizes, the analytical challenges in terms of lateral resolution, sensitivity limits and dedicated nano-preparation have been consistently growing and can only be met by state-of-the-art Auger electron spectrometers (AES), transmission electron microscopy (TEM) analysis, time-of-flight–secondary ion mass spectroscopy (ToF–SIMS) characterization, focused ion beam (FIB) sectioning and TEM lamella preparation via FIB. The depth profiling of GMR sensor full stacks was significantly improved by the ToF–SIMS Cs method.