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Record W2527907091

Experimental and Numerical Study of the Mechanical Aspects of the Stitch Bonding Process in Microelectronic Wire Bonding

2014· dissertation· en· W2527907091 on OpenAlexfundno aff
Alireza Rezvanigilkolaee

Bibliographic record

VenueUWSpace (University of Waterloo) · 2014
Typedissertation
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroelectronicsWire bondingMaterials scienceMechanical engineeringProcess (computing)NanotechnologyEngineeringComposite materialComputer scienceElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The goal of this thesis is to improve the understanding of the stitch bonding process in microelectronic wire bonding. In particular, it focuses on investigating the effect of the process parameters bonding force, scrub amplitude, and skid on experimental bond quality responses, including qualitative (non-sticking, sticking, and tail-lifting) and quantitative (stitch pull force, tail pull force). In addition to the experimental work, a finite element (FE) model is
\ndeveloped for the stitch bonding process using ABAQUS software, and compared with the
\nexperimental observations.
\nFor the first set of experiments, the stitch bonding is performed with a 18 μm diameter Pd
\ncoated Cu (PCC) wire on a “low bondability” Au/Ni/Pd plated quad-flat non-lead (QFN) substrate. Results showed that a high bonding force, a high scrub amplitude, and a positive skid provoke the sticking of the stitch bond and reducing the chance of non-sticking observation. However, such parameters also increase the chance of tail-lifting. As a trade-off for a low bondability substrate, a process parameter combination containing a high bonding force and a high scrub amplitude and a negative skid could ensure a strong enough stitch bonding process with low chance of tail-lifting.
\nFor the second set of experiments, the stitch bonding is performed with a 18 μm diameter
\nuncoated Cu wire on a “high bondability” Ag plated QFN substrate. Statistical analysis of
\nstitch and tail pull force showed that the skid and scrub parameters have a more significant
\ninfluence than bonding force. A positive skid can degrade the stitch pull force, while enhancing the tail pull force. A high scrub amplitude is found to degrade both the stitch and the tail pull forces. The bonding force is shown to improve the stitch and tail pull forces slightly. Performing an optimization, process parameters of 70 gf (687 mN) bonding force, 3 μm scrub amplitude, and zero skid result in acceptable stitch and tail pull forces, along with a reliable stitch bond appearance (low peeling and shallow capillary tool impression).
\nThe influence of the process parameters is significantly different depending on if bonding on
\nlow or high bondability substrates. For example, a positive skid increases the chances of sticking and tail-lifting on low bondability substrate, but it decreases the tail pull force and
\nincreases the tail pull force for high bondability substrate. This indicates that finding a general
\nexperimental rule for understanding the effect of process parameters on the stitch bond quality is difficult if not impossible. In other words, instead of general rule, it is more likely to find individual rules for specific individual applications.
\nTo improve the understanding of stitch bonding a three dimensional (3D) dynamic explicit FE
\nmodel is developed in ABAQUS. The model components and boundary conditions are constructed and applied to reflect the experimental conditions. The bonding force, scrub, and skid are successfully implemented into the model. Mass scaling is applied carefully to save calculation time while ensuring there are no artificial effects of inertia. The model is able to render the conventional responses reported in the past including stress and strain distributions. However, these conventional outputs were not sufficient to provide a correlation between model and experiment. Therefore, new candidate responses were developed and extracted from the numerical results. The new responses are based on accepted welding mechanisms.
\nOne of the mechanisms is interfacial cleaning by frictional energy which is beneficial for
\nbonding. Thus the friction energy accumulated during the simulated bond duration is
\nextracted as a candidate response. For classical cold welding processes, the interfacial surface expansion is a key mechanism, as it opens up cracks in the surface contamination and oxide layers and thereby generates paths to bring the fresh metals together under pressure. Therefore, candidate responses related to surface expansion at the contact interface are extracted from the model.
\nThe complete set of new responses extracted from the numerical model includes contact areas,
\nsurface expansion per areas, frictional energy, and combination of frictional energy combined
\nwith surface expansions per areas. In addition the bond interface is divided into “wedge” and
\n“tail” regions. The model is run for the same DOE cells as used in the first set of experiments
\nand candidate responses are extracted and compared with the experimental observations. By ranking the correlation coefficients of each individual candidate responses, for the first time correlations that are relatively strong are found between a numerical response and experimental observations of stitch bonding. Responses that have correlation coefficients of 0.79 and 0.85 were found for wedge sticking and tail-lifting, respectively. Such relatively strong correlation indicates that the friction enhanced cleaning and the surface expansion mechanisms are proper theories for the current stitch bonding system. These theories can be used for developing similar models for other types of the solid-state bonding processes.
\nBased on the best candidate responses, a procedure to determine numerical process windows is demonstrated for a specific application. Such a window defines the parameter ranges which result in an acceptable stitch bonding process and is an excellent indication of how suitable a process is for mass production. Depending on the application, materials, geometries, and tools, the FE model and process window procedure allow a variety of numerical process windows to be produced and compared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.204
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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