Application of Image Processing to Characterize Patterning Noise in Self-Assembled Nano-Masks for Bit-Patterned Media
In bit-patterned media (BPM), media noise is expected to be dominated by fabrication imperfections and to manifest as island size and spacing fluctuations. Previous work has shown that media noise has a significant impact on the error rates of BPM channels. Thus, having a realistic model of media noise is essential to evaluate the performance of the BPM channels. In this work, we apply image processing techniques to a scanning electron microscope image of a candidate BPM nano-mask, produced by a self-assembly technique, to estimate likely size and location fluctuations of the islands. These statistics may also be useful to characterize the samples of candidate BPM masks. We assume that these observed fluctuations are representative of patterning noise that might result from such a mask. The results indicate that these fluctuations are correlated and can be modeled by Gaussian random processes. Using estimated characteristics of islands size and location fluctuations, we model the correlated media noise and investigate the performance of BPM channels in the presence of such correlated media noise.