A study of GMR read sensor induced by soft ESD using Magnetoresistive Sensitivity Mapping (MSM)
Electrostatic discharge (ESD) can be manifest on giant magnetoresistive (GMR) head either physically (melting of sensor element) or magnetically (degrades in dynamic electrical or quasitest parameters). The latter case is usually suspected as ldquosoftrdquo ESD and known to be a subtle case for study. In this study, we try to differentiate the ldquosoftrdquo ESD from electrical failure, which cause by other means. Ahead with magnetoresistance sensitivity mapping (MSM), which is an additional feature of magnetic force microscope (MFM), the symmetry and sensitivity changes of GMR sensors were further investigated. Advanced analytical tools of atomic force microscope (AFM), MFM and MSM allow unique characterization of head microstructure, and coupled with methodical and systematic failure analysis, may be possible to observe in-situ the previously unknown ESD phenomena unique to GMR head.
Amplitude and asymmetry study using magnetoresistive sensitivity mapping (MSM) on manufacturing ESD failures and ESD simulation experiments
Magnetoresistive Sensitivity Mapping (MSM) is used to investigate hard and soft electrostatic discharge (ESD) damage on anisotropic magnetoresistive (AMR) readers from manufacturing failures and simulation experiments. Unipolar MSM data is compared with sensor output voltage amplitudes which are measured using signals from magnetic transitions written on a high density magnetic tape. A bipolar MSM technique using two oppositely magnetized tips to measure the AMR sensitivity was applied to reveal sensor asymmetry. Multiple domains are observed on sensors exposed to soft ESD. Re-magnetization of the soft ESD damaged head showed the soft ESD damage is irreversible.
Detection of Fractures Based on Directional Filtering in Image Logging
According to the feature of the direction of fractures in image logging, a method of fracture detection in image logging based on directional filtering is presented in this paper. The fracture image is filtered by the corresponding filter based on the local direction of fractures, which enhances the fracture traces in the direction of fractures in image logging. The final fracture traces are extracted by the combination of the results of both directional filtering and the threshold segmentation. Experimental results show this method effectively detect the fractures in image logging.