Maximum-likelihood sequence detector for dynamic mode high density probe storage

Authors:
Naveen Kumar;Pranav Agarwal;Aditya Ramamoorthy;Murti Salapaka
Affiliations:
Dept. of Electrical and Computer Engineering, Iowa State University, Ames, IA;Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN;Dept. of Electrical and Computer Engineering, Iowa State University, Ames, IA;Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 4
Cited 0

A nanotechnology-based approach to data storage

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Bayes-ball: Rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams)

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The "millipede" - nanotechnology entering data storage

IEEE Transactions on Nanotechnology
The Viterbi algorithm and Markov noise memory

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

There is an ever increasing need for storing data in small form factors driven by the ubiquitous use and increased demands of consumer electronics. A new data storage approach that acheives a few Tb per in areal densities, utilizes a cantilever probe with a sharp tip that can be used to deform and assess the topography of a polymer medium. The information may be encoded by means of topographic profiles on the medium. The prevalent mode of using the cantilever probe is the static mode that is known to be harsh on the probe and the media. In this paper, the high quality factor dynamic mode operation, which is known to be less harsh on the media and the probe, is analyzed for probe based high density data storage purposes. It is demonstrated that an appropriate level of abstraction is possible that obviates the need for an involved physical model. The read operation is modeled as a communication channel which incorporates the inherent system memory due to the inter-symbol interference and the cantilever state that can be identified using training data. Using the identified model, 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 other existing schemes and confirms performance gains that can render the dynamic mode operation feasible for high density data storage purposes.