Jump to content

Evangelos S. Eleftheriou

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by Ibmzrl (talk | contribs) at 10:32, 14 November 2016 (Added new award.). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Evangelos Eleftheriou (Template:Lang-el) is a Greek electrical engineer. He is an IBM Fellow and responsible for the Cloud and Computing Infrastructure department at the IBM Research – Zurich laboratory in Rüschlikon, Switzerland, and is also the group leader for the Memory and Cognitive Technologies group in that department.

Education and research interests

Eleftheriou graduated in 1979 from the University of Patras, Greece, with a B.S. degree in Electrical Engineering. He then attended Carleton University in Ottawa, Canada, where he obtained his M.Eng.(1981) and Ph.D. (1985) degrees in Electrical Engineering.

He joined the Research Staff of IBM Research – Zurich laboratory in Rüschlikon, Switzerland, in 1986. He currently heads its Cloud and Computing Infrastructure department.

He holds over 150 patents (granted or pending) and has authored or co-authored about 200 scientific publications.

Research activities

Eleftheriou performed basic research in noise-predictive detection, which found wide application in magnetic recording systems and spurred further research on advanced noise-predictive schemes for a variety of stationary and non-stationary noise sources.[1][2][3] In this context,[4][5] he developed the reduced state sequence detection approach, which is also the basic idea behind the so-called Noise-Predictive Maximum Likelihood (NPML) detection for magnetic recording. This work in its various instantiations, including iterative detection/decoding schemes,[6] is the core technology of the read channel module in hard-disk drives (HDDs) and tape drive systems. The Eduard Rhein Foundation said Eleftheriou had "a pioneering role in the introduction of innovative digital signal processing and coding techniques into hard disk drives".

In 2001, he started to work on a concept that IBM’s 1986 Nobel laureate Gerd Binnig had originated, namely, to use atomic force microscopy to not only image surfaces, but to also manipulate the surface of soft materials, such as polymers, and write information in the form of nanometer-scale indentations. This concept is now known as probe-based storage[7][8][9] or informally as the so-called Millipede Storage. Together with his team, he demonstrated a small-scale, form-factor prototype storage system using thermomechanical probes, which achieved error-free writing and read back of data at an ultrahigh areal density of 840 Gb/in2, then a world record for data storage. The “millipede” work was selected as “Technology of the Year[10]” by the US trade publication IndustryWeek in 2003.

Through this effort improvements were made in the field of nanopositioning research,[11][12][13] a key enabling technology for investigating and engineering matter at the nanometer scale, for a variety of applications that include not only data storage, but also molecular biology, metrology, nano lithography and scanning probe microscopy.

Eleftheriou co-developed the progressive edge growth (PEG) algorithm, a general method for constructing regular and irregular Tanner graphs having a large girth. This algorithm is of great importance in graph theory as well as for constructing powerful short-block-length LDPC codes, a methodology used extensively f in recording and transmission systems[14][15]

Since 2007, he and his team have increasingly focused on phase-change memory (PCM) as a storage-class memory bridging the gap between memory and storage. They have investigated how to store more than one bit per cell or so-called MLC (multi-level cell) PCM. They have successfully tackled the problem of long-term resistance drift in MLC PCM by using novel read-out metrics. Using a new device concept in which the physical mechanism of writing is decoupled from the read process, they were able to eliminate drift; they call this new concept “projected PCM devices[16][17][18]”.

They have also investigated carbon as memory[19][20][21] material,[22][23] focusing in particular on oxygenated amorphous carbon to address the issue of low endurance due to the difficulty of breaking the conductive carbon filaments. In oxygenated amorphous carbon, oxygen is added as a dopant to facilitate the breaking of the carbon filaments because it is known that carbon-based materials, when exposed to oxygen, break down by so-called Joule heating.

Awards and honors

Appointed Fellow of the IEEE, 2001

2005 Technology Award of the Eduard Rhein Foundation, Germany

Appointed an IBM Fellow, 2005

Inducted into IBM Academy of Technology, 2005

IEEE Control System Society’s Control Systems Technology Award, December 2009

Honoris Causa Professor, from the University of Patras, 9 November 2016

References

  1. ^ Kavcic, A.; Moura, J.M.F. (January 1, 2000). "The Viterbi Algorithm and Markov Noise Memory". IEEE Transactions on Information Theory. 46 (1): 291–301. doi:10.1109/18.817531.
  2. ^ Kaynak, M.N.; Duman, T.M.; Kurtas, E.M. (December 2005). "Noise Predictive Belief Propagation". IEEE Transactions on Magnetics. 41 (12): 4427–4434. doi:10.1109/TMAG.2005.857101.
  3. ^ Dee, R.H. "Magnetic Tape for Data Storage: An Enduring Technology". Proceedings of the IEEE. 96 (11): 1775–1785. doi:10.1109/JPROC.2008.2004311.
  4. ^ Coker, J.D.; Eleftheriou, E; Galbraith, R.L.; Hirt, W (1998). "Noise-predictive maximum likelihood (NPML) detection". IEEE Trans. Magnetics. 34 (1): 110–117. doi:10.1109/20.663468.
  5. ^ Eleftheriou, E; Hirt, W. "Noise-predictive maximum-likelihood (NPML) detection for the magnetic recording channel". Proc. IEEE Int'l Communications Conf: 556–560. doi:10.1109/ICC.1996.542258.
  6. ^ Eleftheriou, E; Ölçer, S; Hutchins, R.A. (2010). "Adaptive Noise-Predictive Maximum-Likelihood (NPML) Data Detection for Magnetic Tape Storage Systems". IBM J. Res. Develop. 54 (2). doi:10.1147/JRD.2010.2041034).
  7. ^ Binnig, G.K.; Cherubini, G.; Despont, M.; Duerig, U.T.; Eleftheriou, E.; Pozidis, H.; Vettiger, P. (2010). The ‘Millipede’ - A nanotechnology-based AFM data storage system. Berlin: Springer-Verlag. pp. 1601–1632.
  8. ^ Eleftheriou, E.; Antonakopoulos, T.; Binnig, G.K.; Cherubini, G.; Despont, M.; Dholakia, A.; Dürig, U.; Pozidis, H.; Lantz, M.; Rothuizen, H.; Vettiger, P. "Millipede: A MEMS-based scanning-probe data-storage system". IEEE Trans. Magnetics. 39 (2): 938–945. doi:10.1109/TMAG.2003.808953.
  9. ^ Abramovitch, D.Y.; Andersson, S.B.; Pao, L.Y.; Schitter, G. (2007). "A Tutorial on the Mechanisms, Dynamics, and Control of Atomic Force Microscopes". American Control Conference: 3488–3502. doi:10.1109/ACC.2007.4282300.
  10. ^ Vinas, Tony (December 21, 2004). "Technologies Of The Year -- IBM's Millipede March". No. December. Penton. Retrieved December 14, 2004.
  11. ^ Devasia, S.; Eleftheriou, E.; Moheimani, S.O.R. (2007). "A survey of control issues in nanopositioning". IEEE Trans. Control Systems Technol., Special Issue on “Dynamics and Control of Micro- and Nanoscale Systems. 15 (5): 802–823. doi:10.1109/TCST.2007.903345.
  12. ^ Pantazi, A.; Sebastian, A.; Cherubini, G.; Lantz, M.A.; Pozidis, H.; Rothuizen, H.; Eleftheriou, E. (2007). "Control of MEMS-based scanning-probe data-storage devices". IEEE Trans. Control Systems Technol., Special Issue on "Dynamics and Control of Micro- and Nanoscale Systems. 15 (5): 824–841. doi:10.1109/TCST.2006.890286.
  13. ^ Clayton, G.M.; Tien, S.; Leang, K.K.; Zou, Q.; Devasia, S. "A Review of Feedforward Control Approaches in Nanopositioning for High-Speed SPM". Journal of Dynamic Systems, Measurement, and Control. 131 (6): 061101.
  14. ^ Hu, X.-Y.; Eleftheriou, E.; Arnold, D. (January 2005). "Regular and irregular progressive edge-growth Tanner graphs". IEEE Trans. Inf. Theory. 51 (1): 386–398. doi:10.1109/TIT.2004.839541.
  15. ^ Venkiah, A.; Declercq, D.; Poulliat, C. (April 2008). "Design of Cages with a Randomized Progressive Edge-Growth Algorithm". IEEE Communications Letters. 12 (4): 301–303. doi:10.1109/LCOMM.2008.071843.
  16. ^ Koelmans, W.W.; Sebastian, A.; Jonnalagadda, V.P.; Krebs, D.; Dellmann, L.; Eleftheriou, E. (3 September 2015). "Projected phase-change memory devices". Nature Communications. 6. doi:10.1038/ncomms9181.
  17. ^ Li, J.; Luan, B.; Lam, C. (April 15, 2012). "Resistance Drift in Phase Change Memory". 2012 IEEE International Reliability Physics Symposium (IRPS): 6C.1.1–6C.1.6. doi:10.1109/IRPS.2012.6241871.
  18. ^ Sampson, A.; Nelson, J.; Strauss, K.; Ceze, L. (September 2014). "Approximate Storage in Solid-State Memories". ACM Transactions on Computer Systems. 32 (3, Article 9). doi:10.1145/2644808.
  19. ^ Santini, C.A.; Sebastian, A.; Marchiori, C.; Prasad Jonnalagadda, V.; Dellmann, L.; Koelmans, W.W.; Rossell, M.D.; Rossel, C.P.; Eleftheriou, E. (23 October 2015). "Oxygenated amorphous carbon for resistive memory applications". Nature Communications. 6. doi:10.1038/ncomms9600.
  20. ^ Sebastian, A.; Pauza, A.; Shelby, R.M.; Fraile Rodriguez, A.; Pozidis, H.; Eleftheriou, E. (2011). "Resistance switching at the nanometre scale in amorphous carbon". New J. Phys. 13 (1).
  21. ^ Marks, Paul (November 16, 2015). "The latest advances in carbon computing—and graphene is nowhere to be seen". Ars Technica. Ars Technica. Retrieved November 16, 2015.
  22. ^ Pan, F.; Gao, S.; Chen, C.; Song, C.; Zeng, F. (September 2014). "Recent progress in resistive random access memories: Materials, switching mechanisms, and performance". Materials Science and Engineering: R: Reports. 83: 1–59. doi:10.1016/j.mser.2014.06.002.
  23. ^ Peng, P.; Xie, D.; Yang, Y.; Zang, Y.; Gao, X; Zhou, C.; Feng, T.; Tian, H.; Ren, T.; Zhang, X. (2012). "Resistive switching behavior in diamond-like carbon films grown by pulsed laser deposition for resistance switching random access memory application". Journal of Applied Physics. 111. doi:10.1063/1.3703063.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Evangelos_S._Eleftheriou&oldid=749443311"