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Agent-based modeling of fluidic self-assembly

Agent-based modeling of fluidic self-assembly. Massimo Mastrangeli Complex Systems Summer School 2009 Santa Fe Institute. Context : electronics manufacturing. Success of microelectronics is largely driven by economy of scale Higher production, lower price

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Agent-based modeling of fluidic self-assembly

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  1. Agent-basedmodelingoffluidicself-assembly Massimo Mastrangeli Complex Systems Summer School 2009 Santa Fe Institute

  2. Context: electronics manufacturing • Success of microelectronics is largely driven by economy of scale • Higher production, lower price • Assembly and packaging costs do not scale! • Need for efficient techniques • Standard assembly done by robots… • Fast and precise at large scale • …do not downscale satisfactorily! • Hard to handle many small chips • More efficient techniques -> SA [Mastrangeli et al., J. Micromech. Microeng., in press] (c) Massimo Mastrangeli 2009

  3. Challenge:Realisticmodelsfor passive SA • Components are pre-conditioned to assemble in a pre-designed collective configuration • Static SA, external drive required for mixing and transport • Lock-key driven by physical forces (capillarity, gravity, electro-magnetics, chemical) • Few analytical models exist • Rate equations, reaction-limited • Mean-field-like • How to include real-life? • Component’s excluded volume and momentum, finite assembly space, diffusion-limited reactions, dissipation and mixing, … (c) Massimo Mastrangeli 2009

  4. Proposal:ABM forfluidic SA • Gas-in-a-box model in NetLogo • Finite box and particles, temperature • Viscous drag in fluidic carrier • multiple particle types • Both 2D and 3D • Cooperative collision-driven assembly • Steric factors, redundancy, mixing, transport • Naturally accounts for fluctuations • Real-time statistics and interactions • Also offline in Behavior Space (c) Massimo Mastrangeli 2009

  5. Perspective:tuning and exploration • Tune the model to reproduce data • Explore the parameter space • Maximize yield, minimize time-to-assembly • Encode physical interactions • Driving forces • Analise environmental effects and constraints • Investigate cooperativity, selectivity, sequentiality • Work in progress… Zheng et al. PNAS 2006 (c) Massimo Mastrangeli 2009

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