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Tad Hogg, Ph.D.


Tad Hogg, Ph.D. Member of the Research Staff Hewlett-Packard Laboratories Coordinating Microscopic Robots for Nanomedicine Tad Hogg HP Labs topics microscopic robots ... – PowerPoint PPT presentation

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Title: Tad Hogg, Ph.D.

Tad Hogg, Ph.D.
  • Member of the Research Staff
  • Hewlett-Packard Laboratories

Coordinating Microscopic Robots for Nanomedicine
  • Tad Hogg
  • HP Labs

with Phil Kuekes (HP) Arancha Casal (Stanford
Medical School) David Sretavan (UCSF)
  • microscopic robots
  • physics
  • example task

microscopic robots
  • robots with sizes similar to bacteria
  • a micron
  • capabilities
  • sense, e.g., chemicals
  • compute, e.g., pattern recognition
  • act, e.g., move, release chemicals, communicate
  • plausible extrapolation of current nanotechnology

  • atom (0.1nm)
  • large molecule (1-10nm)
  • virus (102nm)
  • bacteria (103nm)
  • complex cell (104nm)

component, e.g., switch
machine, e.g., computer
conventional semiconductor switches
CPUs 100-1000 times larger
swarm of microscopic devices
104 1012 devices novel applications from
activity of group not any single device
each device size about 1 micron, mass about
10-12 gram with molecular electronic components
system design challenge reliable, useful group
behavior in microscopic environments
  • low Reynolds number fluid flow
  • chemical diffusion
  • Brownian motion

microscopic robots Why?
  • access space and time scales relevant for biology
  • medical research, diagnosis, treatment
  • continuous high-resolution monitoring
  • instead of
  • occasional sampling
  • averaging over many cells behavior
  • study large populations of cells in vivo

Why for industry?
  • e.g., molecular electronics at HP
  • crossbar architecture
  • self-assembled molecular devices
  • for memory and logic
  • identify early niche applications
  • limited computation in tiny volume
  • e.g., for biology
  • NOT more computation than Pentium

microscopic robots How?
  • caveat not yet possible to fabricate
  • approaches
  • engineer biology
  • analogous to domesticating plants animals
  • de novo fabrication
  • analogous to semiconductor fabs
  • must be cheap to make large numbers
  • cf. transistors

How to control?
  • compared to conventional robots
  • different dominant physics
  • much larger numbers of robots
  • wide variety of micro-environments
  • not well-characterized
  • reactive, local control
  • reliability from many simple interactions
  • avoid undesirable emergent behaviors

  • microscopic robots
  • physics
  • example task

physics of microscopic robots
  • surface dominates volume
  • thermal noise noticeable
  • quantum effects not significant

E. M. Purcell, Life at Low Reynolds Number,
American J. of Physics, 453-11 (1977)
physics surface forces
  • high surface to volume ratio
  • high strength, fast dynamics
  • viscous fluid drag, friction
  • force velocity, not F m a
  • inertial forces negligible
  • Newtons F m a

physics thermal noise
  • Brownian motion
  • randomly changes location orientation
  • noticeable for microscopic devices
  • limits sensor accuracy

physics quantum effects
  • superposition
  • uncertainty
  • interference
  • entanglement
  • not significant for cell-size machines
  • unless specialized hardware
  • robots with quantum computers
  • quantum smart matter

power a key constraint
  • to move, communicate, compute,
  • typical task requirements 1-1000pW
  • some sources for this power
  • on-board storage (short-term tasks)
  • glucose (typical blood concentrations)
  • ultrasound

power for 1-micron device
  • 1 picowatt (pW) allows
  • 105 logic operations/sec
  • communicating 104 bits/sec over 100mm
  • with ultrasound
  • moving 1mm/sec through water
  • 1000pW from glucoseoxygen in blood
  • compare 10-1000pW use by cells
  • cells are larger 10mm
  • person at rest uses 100 watts

  • microscopic robots
  • physics
  • example task

evaluate control methods
  • examine various scenarios
  • performance vs. capability tradeoffs
  • e.g., time to finish vs. power use
  • with
  • medical relevance
  • quantified micro-environment
  • low computational cost to simulate

task scenarios
  • enhance immune response to injury
  • find source of chemical signal
  • repair damaged nerves
  • identify axons to connect via graft

start with simple parts of overall task
task respond to injury
  • monitor for chemical signal
  • follow gradient to source
  • coordinate avoid too many responders!
  • identify infectious microbe
  • pass info to attending physician
  • which immune cells cant do

go in, look around, get out, tell me what you
found and then Ill determine what it means
vessels lt0.1mm diameter 10 total blood
volume 95 of 500m2 surface area gt99 of
5x104 km length
  • small vessels
  • exchange chemicals with tissue
  • about 10mm diameter
  • comparable to size of cells

devices within small blood vessels
schematic of one device in 20mm blood vessel
operate in moving fluid crowded with
cells various chemicals fractal branching geometry
cf. artist conceptions often show much more open
a simulation environment A. Cavalcanti,
scenario find chemical source
  • 1012 robots in 5-liter blood volume
  • use about 10-5 of blood volume
  • compared to 40 used by red cells
  • total mass of all robots 0.2 g
  • power to move 10-12 watt
  • so if all move at once 1 watt
  • vs. a person at rest using 100 watts

benefit of communication
  • detect source somewhat downstream
  • much power to swim back upstream
  • vs. communicate to upstream devices

color indicates chemical concentration
flow, 1mm/s
10 mm
30 mm
source on pipe wall, fluid flow (parabolic
profile), diffusion coef. 300mm2/s
comparing control methods
  • time to reach signal source
  • typical chemical diffusion, fluid flow speed,
    vessel size

random motion
time (seconds)
measure follow chemical gradient
number finding source
Adriano Cavalcanti (Unicamp Univ of Campinas,
lessons immune response
  • simple control rules effective
  • redundancy from huge numbers
  • even for source size of just one cell
  • possibly much faster response
  • than immune system
  • devices could act or alert physician

T. Hogg and P. Kuekes, Mobile Microscopic Sensors
for High-Resolution in vivo Diagnostics,
Nanomedicine Nanotechnology, Biology, and
Medicine 2239 2006
task nerve repair
  • approaches
  • regeneration via appropriate chemicals
  • repair via replacement with graft tissue

go in, find damaged axons, tell me what you
find then Ill think about the situation and tell
you what to fix, then well test your
repairs, finally get out
nervous system
  • cells with long axons
  • up to 1m in length

axon injury
synapses lost (Wallerian degeneration)
scenario nerve repair
D. Sretavan et al., Neurosurgery 57635 (2005)
junction with exposed axons (only a few
shown) 10s of microns long and wide
MEMS device
undamaged host
graft, 1cm
undamaged host
in vitro repair demonstrated for single axons
with MEMS in vivo must measure and manipulate
1000 axons in nerve
MEMS microsurgery device
D. Sretavan et al., Neurosurgery 57635 (2005)
1mm3 volume view from below axon cutter at center
repair process
  • remove damaged section
  • replace with graft
  • expose axons in host graft
  • enzymes digest connective tissue
  • place two axons together, electrofuse
  • voltage pulse causes membranes to fuse
  • often gives functional axon

coordinate MEMS nano
104 nanorobots
  • nano identify axon type
  • motor, sensory
  • MEMS nano signal through graft
  • to determine matching axon ends
  • big computer determine axons to fuse
  • nano fuse axons
  • MEMS nano test repairs

physician remains in the loop
performance tradeoffs
  • 1000 axons, 104 robots/junction

to repair more uses more movement hence more
fraction repaired
distance axons moved to fuse (mm)
lessons nerve repair
human micro device nano swarm
  • general strategy
  • use devices for detailed look around
  • then compute what to do
  • incorporate relevant clinical constraints
  • use devices as tiny hands
  • MEMS for tissue-scale manipulation
  • fast accurate treatments
  • physician can monitor and control progress

T. Hogg and D. Sretavan, Controlling Tiny
Multi-Scale Robots for Nerve Repair, Proc. of
  • difficult
  • cant yet build devices to test
  • many unknown biophysical parameters
  • partial answer robustness
  • achieve task with multiple plausible
  • device capabilities
  • control methods
  • range of task parameters

R. Freitas Jr, Nanomedicine IIA
Biocompatibility, 2003
  • biocompatibility
  • time minutes, hours, days, .
  • depending on task
  • reliable controls
  • allow for errors
  • sensor noise, broken devices,

further info
  • T. Hogg, Designing Microscopic Robots for Medical
    Diagnosis and Treatment, Nanotechnology
    Perceptions 363-73 (2007)
  • T. Hogg and D. Sretavan, Controlling Tiny
    Multi-Scale Robots for Nerve Repair, Proc of
    AAAI05, 2005
  • www.hpl.hp.com/research/idl/people/tad
  • R. Freitas Jr., www.nanomedicine.com
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