Nano Engineering & Storage Technology Group Research Presentation Tom Thomson Nano Engineering & Storage Technology Group School of Computer Science University of Manchester http://nest.cs.manchester.ac.uk/
NEST Group Academics: Tom Thomson Jim Miles Ernie Hill Paul lnutter Milan Mihajlovic PDRAs: Craig Barton, (+2 in next 9 months) PhD students: Antonios Oikonomou, Raymon White, Chris Tian, Georg Heldt, Jen Talbot, David Shepherd, Smaragda Zygridou, Sarah Varey, Stefan Goodwin, Nick Clark, August Johansson, Rhys Griffiths, Andrea Verre, Jack Warren CMN: Fred Schedin, Ian Stutt, Richard O Conner 2/31
What do we do? - Nano Engineering & Data Storage Nano Engineering Miniaturisation scaling and new paradigms Graphene devices Graphene FET Data storage New ideas in magnetic recording What comes next? Numerical Modelling Micromagnetic simulations of recording systems Multiphysics (modelling at different length scales) x 10-8 10 8 6 4 2 Big data from big experiments (analysis of x-ray & neutron data) 50nm 1 µm (a) Hall device 0 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 x 10-7 Simulation of recorded data pattern
Centre for Mesoscience and Nanotechnology (CMN) Significant investment in upgrading capability and capacity of cleanrooms in 2009/2010 (~ 2.0m) Fabrication Raith e-beam Laser writer Ion milling Deposition Optical lithography Oxford dplasmalab Lesker sputtering Leybold evaporation Moorfield evaporation State of the art magnetic characterisation Vector VSM Magnetic force microscopy Observation Scanning Electron microscopy Scanning probe microscopy SQUID XL7 Magnetic film deposition Electron-beam lithography Observation and measurement
Magnetic recording: The way forward Three potential paths are being explored to increase magnetic data storage density beyond 1 Tbit/in 2 Extension of conventional or not so conventional perpendicular media Bit patterned media Thermally assisted recording Developing the scientific understanding that underpins technological advancement has some common themes Anisotropy distributions Relationship between magnetisation switching and thermal stability (exchange springs) Finite size effects in sub 5 nm magnetic grains SANS data
Magnetic recording media (Physical) Conventional perpendicular in today s drives Discrete Track Patterning to reduce track interference Thermally assisted Bit Pattern Media One bit per island recording ~300 Gbit/in 2 820 Gbit/in 2
Graphene Transistors Strongly Layered Material Pull Out One Atomic Plane 20 m optical photograph Au contacts SiO 2 Si 2D Graphene Layer
Graphene nano-circuits Gated nano-contact in graphene sheet e-b lithography Channel can be switched off at room temperature 6 Contact few nm 300 K ( S S) 4 10 nm 2 E = v F h/2 0 0 02 0.2 04 0.4 /2D gate (V)
Broad range of activities iti in NEST The Centre for Mesoscience & Nanotechnology (CMN) run jointly by NEST and the condensed matter group in physics acts as a focus for interdisciplinary work and collaborations with Materials Science, Chemistry, Physics etc. Project highlights: Exchange spring materials for data storage Tom Thomson, Chris Morrison & Craig Barton Graphene and magnetism Ernie Hill & Chris Tian Recording physics of patterned media - Jim Miles & Jen Talbot Fabrication, Optical and AHE characterisation of BPM Paul Nutter & Marios Alexandrou Efficient finite element e e micromagnetic c models of nanomagnetic ag materials Milan Mihajlovic, Jim Miles & David Shepherd Data recovery and error correction in HDDs Paul Nutter & Lizzy Shi Anisotropy distributions in patterned media Tom Thomson & Georg Heldt (Laura Heyderman) Small angle neutron scattering (SANS) Tom Thomson & Steve Lee Patterned nanostructures NiFe graphene film NiFe 200nm Graphene devices Hitachi GST Perpendicular recording
12000 OK so how does (conventional) computer science SPT Helix contribute? Modelling of devices and processes Atomistic modelling Finite element modelling Micromagnetics Fluid dynamics Multiscale models crosstrack [nm] 250 200 150 100 50 0-50 -100-150 -200 3000 2000 3000 4000 4000 6000 5000 6000 9000 11000 5000 7000 8000 10000 2000 11000 12000 10000 3000 9000 11000 11000 10000 8000 7000 3000 4000 2000 4000 5000 6000 8000 10000 11000 9000 6000 7000 10000 5000 2000 11000 11000 3000 9000 12000 8000 10000 3000 2000 4000 6000 7000 2000 5000 4000-250 -300-200 -100 0 100 200 downtrack [nm] 3000 2000 0 FEM model of head field 16000 14000 12000 10000 8000 6000 4000 2000 0-2000 -4000-6000 Extracting information from large scale data X-ray / neutron scattering Image reconstruction TEM tomography Deconvolution of AFM surface maps SAXS data
Paul Nutter (IT119) Modelling of Hall devices for investigating i i the switching behaviour of magnetic nanoislands Investigating bit-error -rate performance in future hard disc systems
Milan Mihajlovic (IT202) Multiphysics py modelling (multi-length scale) Slice at z = 0 (middle) Numerical modelling and simulation of continuous multiphysics systems (fluid-structure interaction, micromagnetics, circuit integrity simulations) Numerical modelling of multi-scale systems involving colloidal dispersions of nano-particles and nanocarbons Slice at z 0 (middle) (thickness 1 mm) y axis (mm) Temp (K) Iterative solution techniques for large linear systems that arise in simulations of continuous physical systems on a computer; Inherently multi-disciplinary projects with strong collaborative links in Schools of Mathematics, MACE and Materials Science. Points of instability X axis (mm) Snapshot of the temperature field in a cryogenic experiment of liquid id helium at 4K in a chamber heated from below. The flow undertakes an instability called the skewed varicose instability where parallel convective rolls buckle and then merge together.
Ernie Hill (IT118) Graphene Memory Devices Graphene is single layer of carbon atoms What would memory devices made from Graphene look like? Can we simulate their performance? Characterising nanoscale surfaces in 3D Medical implant application Cells can see nanoscale features How can surfaces be characterised to quantify test results? (What parameters are important random features, regular features, rentrant t surfaces etc.)
Jim Miles (IT114/KB2.123) 123) Measurement of data storage in prototype materials to identify error mechanisms Real time control of complex hardware Micromagnetic models of data storage in magnetic nanostructures Predicting error rates in very low error probability systems Projects: Information coding for identification of data-dependent error mechanisms Models of rare events in magnetic storage systems Finite Element Modeling nanostructured magnetic materials
Tom Thomson Information from large central facility experiments - how do materials work at the nano-scale? Magnetisation dynamics of data storage materials How fast is data stored? What controls the speed? How can we make it faster? SANS data Co-Planar waveguide
New activities Higher tunneling current Lower tunneling current Spintronics i Using the electron s spin together with its charge to process and store if information Create new tunable miniature oscillators for mobile communications Antiferromagnet Ferromagnet Ru AF coupling layer Ferromagnet fixed (P 1 ) Tunnel barrier (MgO) Ferromagnet free (P 2 ) Substrate TMR - R/R = P 1 P 2 /(1-P 1 P 2 ): 20 50% Saturation field 10 30 Oe Storage Write/read layer IBM Almaden Research Center
Where do we live? IT building in 1980 s IT building Today National Graphene institute
Questions http://nest.cs.manchester.ac.uk/