IBM Research – Brazil: An Introduction
Transcript of IBM Research – Brazil: An Introduction
IBM Research – Brazil
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IBM System 360The machine that definedthe computer industryand the modern IBM
IBM System 360SLT module6 transistors,4 resistors
What does IBM do?
1964 Solid Logic Technology
Chip (POWER 7)1.2 billion transistors/chipEmbedded DRAM190 watts max
Watson System360 Power7 chips80KW / 80 Teraflops1000Mflops/W
2012 Watson
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Advances in Technology
Source: Kurzweil 1999 – Moravec 1998
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Science defines the future of Technology
Copper
NanophotonicSwitch
Nobel Prize, STM
AtomicManipulation
Carbon Nanotube
Transistors
SelfAssembly
NanotubeIC
MolecularProcessing
Atomic Storage
Slowing Speed of
Light
HighestResolution
EM
Time
Com
plex
ity
SOI
StrainedSilicon
Dual Core
Immersion
Frozen SiGe Chip
High-k
eDRAM
3D ChipStacking
Airgap
Chemically Amplified
Resists
IBM has a long history of making translating, fundamental Silicon & nanotechnology discoveries and innovation into products
US$ 6B spent annually in R&D
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Scientific & Technological Achievements
2012 20th Consecutive Year of Patent Leadership2011 Watson System2009 Nanoscale Magnetic Resonance Imaging (MRI) 2008 World’s First Petaflop Supercomputer 2007 Web-scale Mining2006 Core Extensible Markup Language (XML) Standards2006 Services Science, Management, Engineering (SSME)2005 Cell Broadband Engine2004 Blue Gene/L 2003 Carbon Nanotube Transistors 2000 Java Performance1997 Copper Interconnect Wiring 1997 Secure Internet Communication (HMAC, IPsec)1997 Deep Blue 1994 Design Patterns1994 Silicon Germanium (SiGe)1990 Statistical Machine Translation1987 High-Temperature Superconductivity 1986 Scanning Tunneling Microscope1980 Reduced Instruction Set Computing (RISC) 1971 Speech Recognition 1970 Relational Database 1967 Fractals 1966 One-Device Memory Cell 1957 FORTRAN 1956 Random Access Memory Accounting Machine (RAMAC)
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Distinguished scientists
10 US National Medals of
Technology
5 National Medals of Science
Over 400 Professional
Society Fellows
64 Members in National Academy
of Engineering
22 Members in National Academy
of Sciences
5 Nobel Laureates 6 Turing Awards
11 Inductees in National Inventors
Hall of Fame
Scanning Tunneling Microscope
Electron Tunneling Effect
High Temperature Superconductivity
Nuclear Magnetic
Resonance Techniques
Basis for MRI today
SiGe
Copper Chip Technology
DRAM
Excimer Laser
High Performance ComputingFirst woman recipient in the history
of this prestigious ACM award
AAAS ACM ACS
APS AVS ECS
IEEE IOP OSA
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A Diversity of Disciplines From Atoms to Service Science
ElectricalEngineering
Computer Science
Behavioral Science
Materials ScienceChemistryPhysicsMathematical
Science
Service Science
BusinessInnovation
TechnologyInnovation
Social Innovation
Demand Innovation
Science & Engineering
Business & Management
Social & Cognitive Sciences
Economics & Markets
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IBM Research: Global and Vertically Oriented
Almaden (1986/1952)San Jose, CA
Watson (1961)Yorktown Heights, NY Zurich (1956)
Rueschlikon, Switzerland
Tokyo (1982)Yamato, Japan
Haifa (1972)Haifa, Israel
China (1995)Beijing, China
Shanghai (2008)
India (1998)Delhi, India
Brazil (2010)Sao Paulo &Rio de Janeiro
First NewResearch Lab in 12
Years
Austin (1995)Austin, TX
Australia (2010)Melbourne, Victoria
Brazil (2010)Sao Paulo &Rio de Janeiro
Africa (2012)Nairobi, Kenya
Dublin (2011)Dublin, Ireland
Nanotechnology
Processing
Workload-Optimized Systems &
Supercomputing
Cloud
Services Tools
Analytics
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The World is our Lab: 12 Labs Worldwide in 10 Countries
China1995
Watson1945
Almaden1952
Austin1995
Tokyo1982
Haifa1972
Zürich1956
India1998
Dublin2012
Australia2012
Brazil2011
IBM Research worldwide has ~4000 research staff member with diversity of disciplines.
Africa2013
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IBM Research – Brazil view from our Rio de Janeiro lab
Mission: To be known for our science and technology and vital to IBM, Brazil, our clients in the region and worldwide
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IBM Research - BrazilResearch Focus Areas
– Natural Resources Solutions– Systems of Engagement– Smarter Devices– Social Data Analytics
Underlying Research Areas: – Analytics & Optimization – HPC & Computational Science– Distributed Systems & Cloud Computing – Mobile technologies – Physics, Chemistry, Mathematics & Engineering – Semiconductor Packaging – Service Science– Social Science, Design & Human Computer Interaction
IBM @ Av. PasteurRio de Janeiro
IBM @ Rua TutóiaSão Paulo
A team of World Class Researchers in connection with global IBM Research as well as academic communities
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IBM Research - Brazil: Research Groups
Natural events, oil & gas, logistics, and sustainability
Smarter city, citizen engagement, community integration, and education
Large scale service systems operations, optimization, and integration in context of social enterprise
Micro- and nano- technologies and materials aimed at addressing smarter planet challenges
Natural Resources Solutions
Systems
Of Engagement
Social Data
Analytics
Smarter
Devices
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Natural Resources Solutions
Mission: Create industry leading solutions and platforms with innovative data-driven, physically-driven and people-driven analytics
Academic Areas of Interest Applied Math Computational Sciences High Performance Computing Data visualization
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Systems of Engagement
Mission: Promote social engagement, health, urban mobility, the inclusion of people with disabilities, and economic development.
Academic Areas of Interest Education & Universal Design Healthcare Biodiversity & Sustainability Mobile and Ubiquitous Computing
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Social Data Analytics
Mission: Reinvent large scale service systems, operations, and enterprises.
Academic Areas of Interest Service Sciences & Design Distributed Computing Data & Graph Mining Information Visualization Analytics & Optimization AI – Simulation & Machine Learning Human Computer Interaction, Design
& Social Sciences
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Smarter Devices
Mission: To conduct research in micro- and nanotechnologies and materials supporting Brazilian and global industries.
Academic Areas of Interest Physics Chemistry Fluidics Nanotechnology Electrical Engineering Electronics
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Areas of Interest
Microfluidics in Health Care
Enhanced Oil Recovery
Nanotechnology
Electronic Packaging
Micro and Nano Technology
Porous Rock Pore/Network modeling
and microfludics
Prototypes and SensorsCMOS based devices, MEMS and
Sensors
Smarter Materials
Polymer DesignEOR materials
Computational ModelingMultiscale Modeling, Fludics
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Microfluidics in HealthcareSIX Semicondutores (HQ: Rio d.J., plant: Belo Horizonte, MG)
– Founded in 2012 in a public private partnership which includes IBM
– Most advanced semiconductor mfg. company of the Southern hemisphere with 130 & 90 nm (IBM’s 7RF & 8RF technology and MEMS) with 360 WSPD (wafer starts per day) on 200 mm wafers.
– Products are customized integrated circuits with mixed signal /hybrid technology for industry and health care
– Wafer fabrication (130 nm) will begin in Brazil in 2015
Joint program BRL with SIX Semi
– Technology development project for microfluidics bio- & environmental sensor devices
– Technology is developed in IBM Research labs (BRL e ZRL) and focuses on control of reagent flow and fixing analytes in a specific place
Total sample volume: ~2µL
Industrial complex of SIX Semi in Ribeirão das Neves, MG (2013)
Microfluidics device manufactured at ZRL
Modeling of electrodes in a microfluidics device
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Microfluidics for Rock Characterization
Test simple and complex fluids in microfluidic devices of various wettability characteristics, chemistries and complexities
Single Channel Multi-Channel Reservoir on a Chip:Actual Rock Structure
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Multiscale Modeling for Enhanced Oil Recovery
MolecularDynamics
FluidFlow
Quantum Mechanics;Molecules
Surfaces
Porous media
Reservoir simulation
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Quantitative evaluation of flow fields using μPIVmeasurements and LBM simulations
P. W. Bryant1
R. F. Neumann1
M. J. B. Moura1
M. Steiner1
M. S. Carvalho2
C. Feger1 1 IBM Research – Brazil2 PUC – Rio
http://arxiv.org/abs/1407.5034
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Outline
Introduction
Literature Review
Computational Methods
Experimental Methods
Results
Examples
Conclusion
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Introduction
What is Microscopic Particle Image Velocimetry (µPIV)?An experimental method for measuring fluid flow in microscale.
How does it work? Fluid seeded with tracer (fluorescent) particles. Particles are excited with a laser and emit light. Emitted light is collected by a CCD. Consecutive snapshots allow determination of particle velocities. Flow velocity field is obtained.
Where is it used? Microfluidics Microelectronics Healthcare Oil & Gas Chemistry ...
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Introduction
Synchronizer
Objective
Camera
Laser
Fluid with particlesMicrocapillary
Dichromatic Mirror
In Out
MICRO-PIV
Microscopic
Syringe pump
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Introduction
Synchronizer
1st Image (t)Objective
Camera
Laser
Fluid with particlesMicrocapillary
Dichromatic Mirror
In Out
MICRO-PIV
Microscopic
Syringe pump
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Introduction
Synchronizer
1st Image (t)Objective
Camera
Laser
Fluid with particlesMicrocapillary
Dichromatic Mirror
In Out
2nd Image (t + ∆t)
MICRO-PIV
Computer
Microscopic
Syringe pump
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Introduction
Peak at the net particle displacement
1st Image (t) 2nd Image (t+∆t)
1) Image acquirement step
3) Processing step
-
=
2) Pre-processing step
particles +background
background
particles
Background subtraction
4) Post-processing step
outlier vectors → local mean vectors
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Ensemble Average over Velocity Vectors
Frame A(t = t0 )
Frame B(t = t0+∆t )
Image Sequence
1
2
3
N AN
A1
Average Velocity
CorrelationRAB
Peak Search
RA1B1
RANBN
RA2B2
RA3B3
+
+
+
+
A2
A3
BN
B1
B2
B3
∗
∗
∗
∗
Algorithm
Introduction
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Frame A(t = t0 )
Frame B(t = t0+∆t )
Image Sequence
1
2
3
N AN
A1
CorrelationRAB
Peak Search
RA1B1
RANBN
RA2B2
RA3B3
+
+
+
+
A2
A3
BN
B1
B2
B3
∗
∗
∗
∗
<RAB>Average Correlation
Algorithm
RA1B1
RA2B2
RA3B3
RANBN
<RAB>
IntroductionEnsemble Average over Correlation Functions
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Literature Review
450 nm resolution
Exp. setup
Curve fit parameters: - flow rate (pump) - width (channel)
1999
How can we explain the discrepancies... ???
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Literature Review2000
Exp. setup
Out-of-focus particle images
Depth of Field
Measurement depth
Typical values for δzm
Contributions from out-of-focus particles... ???
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Literature Review
Exp. setup
Finite sampling region
Depth of correlation
2
Weighting function
Finite sampling region... OK !!! =)Is this cumbersome formula the ultimate truth... ???
2000
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Literature Review2011
Convoluted correlation function
Theory x Experiments
Velocity decrease as a function of DOC
Spatial average does not work... ???
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Literature ReviewNormalized profile
@ center
@ walls
Several experimental setups
Several pre-/post-processing methods
2012
Agreement between theory and experiments... ???
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Literature Review2012
Tracers vs Red Blood Cells Exp. setup
Flow rate determination
Depth of correlation
Flow rate determination is parameter-dependent... ???
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Literature Review2013
Straight channels
Experiment
rescaled profile
Simulation
Exp. setup
Experiment Simulation
Sierpiński pattern
- rescaled simulation to the experimental average.
- velocity data taken at center
Qualitative comparison... ???
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Computational Methods
Boltzmann Equation Boltzmann Equation with BGK approximation
Transforming Boltzmann Equation as dimensionless
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Computational Methods
Lattice Boltzmann Method
Collision
Streaming
Computational algorithm
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Computational Methods
Higher Reynolds number
Porous mediaLaminar flow
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Experimental Methods
µPIV System Manufactured by TSI Incorporated Controlled by Insight 3G/4G software by TSI Inc. Inverted microscope IX71S1F-3 by Olympus 10x/0.3 air objective UPlanFL-N by Olympus 2x projection lens by Olympus 1376 x 1024 pixels CCD Sensicam 630166 by PowerView 2 pulsed Nd:YAG lasers Gemini PIV-15 by NEW WAVE Laser pulse synchronizer 610034 by TSI Inc.
Microfluidic chip Glass microfluidic device by Dolomite Centre Ltd: straight channel with an elliptical cross section (50 µm and 55 µm semi-axes) 14% aqueous solution of 1 µm fluorescent particles by Thermo Scientific Syringe pump 11 Elite 704501 by Harvard Apparatus: flow rates from 25 to 100 µl/h
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Results
Exp. setupSampling Volume
SEM image
∆t = 500 µs
55 µm
50 µm
Depth of Field ~20 µm
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Results
Average over SV
Finding c and δ
Fit residual minima
Best fit
kinks
Simulated velocity
SV-channel intersections
Flow field measurement
32x32 pixelwindows~ 5.12 µm
100 µl/h
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Results Flow rate
Channel geometry
Theoretical velocity field
Sampling Volume
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ExamplesRobustness against camera misalignment
camera
misaligned SV
x
Determine c(x) → θ = 0.6˚
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ExamplesRobustness against noisy data
Dust particle on microfluidic chip
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ExamplesRobustness against irreproducibility
Before disconnecting the pump After reconnecting the pumpand refocusing the microscope
Two measurements on the same channel and with the same flow rate
Moving the channel and refocusing changes the location of the SV and, hence, the measured velocity profile.
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ExamplesChanging processing algorithms
Ensemble average over correlation functions Ensemble average over velocity vectors
Velocity profile processed from the exact same set of images, but with different algorithms
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Examples
Focalplane
Microscopeobjective
Laser beam
Analysis of Scanning PIV Poiseuille profile
Kloosterman et al., 2011 Maximum velocity
Maximize velocity for c
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Conclusion
A simple spatial average over the Sampling Volume suffices to explain the discrepancies between expected and measured velocity profiles.
The near-wall features such as kinks provide extra information that allow the full determination of flow rates unknown to the experimenter.
The Sampling Volume approach provided a straightforward interpretation of the measured data and was able to reproduce the experimental profile from wall to wall.
µPIV measurements can be made quantitative without using post-processing.
This approach is robust against the most common sources of experimental uncertainty.
The Scanning PIV procedure fails to locate the center of the channel for large DOC.
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Acknowledgements
Michael Engel from IBM Research – Watson for the SEM images.
Diney Ether from LPO – UFRJ for helping with the calibration.
José Florián from PUC – Rio for help with the µPIV equipment.
Angelo Gobbi from LMF - LNNano for profilometer measurements
Contact: [email protected]