Maximum-Likelihood Sequence Detector for Dynamic Mode High Density Probe Storage

Abstract

There is an increasing need for high density data storage devices driven by the increased demand of consumer electronics. In this work, we consider a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per in.2 . The prevalent mode of using the cantilever probe is the static mode that is harsh on the probe and the media. In this article, the high quality factor dynamic mode operation, that is less harsh on the media and the probe, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state.We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes.

AFM Imaging?Reliable or Not?: Validation and Verification of Images in Atomic Force Microscopy

“…Experimental bit error rates (BERs) for Bahl-Cocke-Jelinek-Raviv (BCJR), Viterbi, locally most powerful (LMP), and generalized likelihood ratio test (GLRT) for bit widths varying from 60 to 300 ns. There is a very marginal difference between the LMP and GLRT curves that is not visible in the graph but LMP does perform better than GLRT [25].…”

“…Since both control and communication theories have elaborate frameworks to identify, model, and design to mitigate the effects of uncertainties in modeling, they are particularly relevant for identifying artifacts and providing confidence measures in AFM. Mean vector for two state transitions for 300 ns bit width from experimental data where "1100" and "1101" represents transition from state "110" to state "100" and "101," respectively [25]. Murti V. Salapaka received the B.Tech.…”

“…In [25], an approach based on perspectives from communication systems is shown to address the memory issue. Figure 11 shows a communication-theoretic model of TfAFM.…”

Lower bound for ML sequence detection in ISI channels with Gauss Markov noise

“…This data-dependent noise dominates the white-noise component in a read back signal and precludes the usage of conventional detectors developed for an ISI channel with white noise. In current nanotechnology based probe storage technology, similar noise models and corresponding detectors have also been developed which have been found to have significantly improved performance compared to the state of the art [3]. It is imperative to take the data dependent noise into consideration while designing the detection strategy for obtaining significant performance gains.…”

“…The factorization of the PEP given by (3) for our channel model is possible because the autoregressive filterb is not dependent on the input bit sequence. If the autoregressive filterb(a k−I k ) becomes data dependent, it does not appear to be possible to write the PEP in the form given in(3). In this case, the inverse of the autoregressive filter of the noise process n k , [−b(a…”