ibm research – brazil: an introduction

IBM Research – Brazil

© 2014 IBM Corporation1

IBM Research – Brazil: An Introduction

July, 2014



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



Advances in Technology

Source: Kurzweil 1999 – Moravec 1998



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



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)



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

http://www-03.ibm.com/chips/photolibrary/photo10.nsf/WebViewNumber/4766EDB60C5E7A31852569EC00686B9B



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



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



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



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



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



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 nanotechnologies and materials aimed at addressing smarter planet challenges

Natural Resources Solutions

Systems

Of Engagement

Social Data

Analytics

Smarter

Devices



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



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



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



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



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



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



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



Multiscale Modeling for Enhanced Oil Recovery

MolecularDynamics

FluidFlow

Quantum Mechanics;Molecules

Surfaces

Porous media

Reservoir simulation



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



Outline

Introduction

Literature Review

Computational Methods

Experimental Methods

Results

Examples

Conclusion



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 ...



Introduction

Synchronizer

Objective

Camera

Laser

Fluid with particlesMicrocapillary

Dichromatic Mirror

In Out

MICRO-PIV

Microscopic

Syringe pump



Introduction

Synchronizer

1st Image (t)Objective

Camera

Laser


Dichromatic Mirror

In Out

MICRO-PIV

Microscopic

Syringe pump



Introduction

Synchronizer

1st Image (t)Objective

Camera

Laser


Dichromatic Mirror

In Out

2nd Image (t + ∆t)

MICRO-PIV

Computer

Microscopic

Syringe pump



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



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



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



Literature Review

450 nm resolution

Exp. setup

Curve fit parameters: - flow rate (pump) - width (channel)

1999

How can we explain the discrepancies... ???



Literature Review2000

Exp. setup

Out-of-focus particle images

Depth of Field

Measurement depth

Typical values for δzm

Contributions from out-of-focus particles... ???



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




Convoluted correlation function

Theory x Experiments

Velocity decrease as a function of DOC

Spatial average does not work... ???



Literature ReviewNormalized profile

@ center

@ walls

Several experimental setups

Several pre-/post-processing methods

2012

Agreement between theory and experiments... ???




Tracers vs Red Blood Cells Exp. setup

Flow rate determination

Depth of correlation

Flow rate determination is parameter-dependent... ???




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... ???




Boltzmann Equation Boltzmann Equation with BGK approximation

Transforming Boltzmann Equation as dimensionless




Lattice Boltzmann Method

Collision

Streaming

Computational algorithm




Collision

Streaming




Higher Reynolds number

Porous mediaLaminar flow



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



Results

Exp. setupSampling Volume

SEM image

∆t = 500 µs

55 µm

50 µm

Depth of Field ~20 µm



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



Results Flow rate

Channel geometry

Theoretical velocity field

Sampling Volume



ExamplesRobustness against camera misalignment

camera

misaligned SV

x

Determine c(x) → θ = 0.6˚



ExamplesRobustness against noisy data

Dust particle on microfluidic chip



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.



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



Examples

Focalplane

Microscopeobjective

Laser beam

Analysis of Scanning PIV Poiseuille profile

Kloosterman et al., 2011 Maximum velocity

Maximize velocity for c



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.



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]



IBM Research – Brazilhttp://www.research.ibm.com/brazil/

Thank You!

ibm research – brazil: an introduction

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