Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Mechanical Engineering

Major Professor

Nathan B. Crane, Ph.D.

Committee Member

Nathan D. Gallant, Ph.D.

Committee Member

Rasim Guldiken, Ph.D.

Committee Member

Vinay K. Gupta, Ph.D.

Committee Member

George S. Nolas, Ph.D.


Electronics packaging, Measurement, Microassembly, Modeling, Performance evaluation, Surface tension


The goal of the present work is to develop, and evaluate a parametric model of a basic microscale Self-Assembly (SA) interaction that provides scaling predictions of process rates as a function of key process variables. At the microscale, assembly by “grasp and release” is generally challenging. Recent research efforts have proposed adapting nanoscale self-assembly (SA) processes to the microscale. SA offers the potential for reduced equipment cost and increased throughput by harnessing attractive forces (most commonly, capillary) to spontaneously assemble components. However, there are challenges for implementing microscale SA as a commercial process. The existing lack of design tools prevents simple process optimization. Previous efforts have characterized a specific aspect of the SA process. However, the existing microscale SA models do not characterize the inter-component interactions. All existing models have simplified the outcome of SA interactions as an experimentally-derived value specific to a particular configuration, instead of evaluating it outcome as a function of component level parameters (such as speed, geometry, bonding energy and direction). The present study parameterizes the outcome of interactions, and evaluates the effect of key parameters. The present work closes the gap between existing microscale SA models to add a key piece towards a complete design tool for general microscale SA process modeling.

First, this work proposes a simple model for defining the probability of assembly of basic SA interactions. A basic SA interaction is defined as the event where a single part arrives on an assembly site. The model describes the probability of assembly as a function of kinetic energy, binding energy, orientation and incidence angle for the component and the assembly site. Secondly, an experimental SA system was designed, and implemented to create individual SA interactions while controlling process parameters independently. SA experiments measured the outcome of SA interactions, while studying the independent effects of each parameter.

As a first step towards a complete scaling model, experiments were performed to evaluate the effects of part geometry and part travel direction under low kinetic energy conditions. Experimental results show minimal dependence of assembly yield on the incidence angle of the parts, and significant effects induced by changes in part geometry. The results from this work indicate that SA could be modeled as an energy-based process due to the small path dependence effects. Assembly probability is linearly related to the orientation probability. The proportionality constant is based on the area fraction of the sites with an amplification factor. This amplification factor accounts for the ability of capillary forces to align parts with only very small areas of contact when they have a low kinetic energy. Results provide unprecedented insight about SA interactions. The present study is a key step towards completing a basic model of a general SA process. Moreover, the outcome from this work can complement existing SA process models, in order to create a complete design tool for microscale SA systems.

In addition to SA experiments, Monte Carlo simulations of experimental part-site interactions were conducted. This study confirmed that a major contributor to experimental variation is the stochastic nature of experimental SA interactions and the limited sample size of the experiments. Furthermore, the simulations serve as a tool for defining an optimum sampling strategy to minimize the uncertainty in future SA experiments.