Development of Scalable Pathways for Addressing Constraints of Emerging Manufacturing Processes

Abstract

The goal of this Ph.D. study was to apply first-principle physics, computational fluid dynamic (CFD) simulation, scientific experimentation, model development and process design to develop scalable pathways for addressing critical constraints of emerging manufacturing processes, which can benefit semiconductor, specialty chemicals and pharmaceutical industries. This goal was accomplished by studying four research projects to investigate four key objectives, including development of a model for boosting material utilization efficiency (MUE) in organic vapor phase deposition (OVPD), scalable hardware design for effective vapor mixing and substrate heating management in OVPD, development of a pathway for cost-effective micro-LED assembling and understanding interactions energies and numerical demonstration of electrically directed particle trapping on a charged line. To develop a pathway for boosting utilization efficiency in OVPD, an MUE model was developed, the model insights were numerically and experimentally tested and corroborated. Based on the results, area ratio (substrate to chamber) drives MUE and utilization efficiency can be boosted by engineering thermal boundary layer in additional planes. This film deposition by thermal boundary layer engineering was experimentally demonstrated on practically useful substrates. Therefore, a system configuration that can deliver >75% MUE in OVPD was proposed. Scalable hardware design method was developed by analytically and numerically investigating the effects of process conditions on vapor transport, allowing for assessment of criteria needed for effective vapor mixing and substrate heating management. Based on the results, a scalable method for predicting hardware aspect ratio needed to realize effective vapor mixing was proposed. Pathways for cost-effective microparticle assembling were developed by identifying and quantifying physical interactions acting on the particles in suspension, investigating the dependence of these energies on particle shape (sphere and cube), chip size (0.2 - 50.2 µm) and fill factor (0.25 - 0.95) as well as their relative contributions to the overall free energy change. These energies were determined by using analytical and numerical methods. The results show interactions due to electrostatics and entropy of mixing to dominate those of Lifshitz-van der Waals and gravitational energies for different chip size and shapes. Free energy values from thermodynamic assessment of the process reveal surface energies and surface potential on the receiving cavities as clear pathways for chip assembling, leveraging dominance of interfacial Lewis acid-base interaction and entropic contribution, respectively. Thus, these findings and developed pathways can inform engineering chip-processing system for cost-effective assembling. To understand interaction energies during electrically directed particle trapping of particles to a charged line (which models binding of proteins to deoxyribonucleic acid (DNA) strand), energies of interactions, including Lifshitz-van der Waals (LW), Brownian, electrostatics, entropic contribution and gravity, were analytically and numerically quantified to assess overall thermodynamic feasibility of the process. Based on the results, comparative assessment of the energies reveals electrostatic interaction and entropic contribution are dominant contributors, which are both tunable by potential applied on the nanowire. Further, to capture the trajectories of the particles during the process, electrophoretic behavior of the particle under all four relevant competing forces (gravity, drag, electric and Brownian) was modeled under different system settings. The dynamic confinement of particles to charged lines was both numerically and experimentally demonstrated. In conclusion these original contributions from this Ph.D. present pathways for addressing constraints of emerging manufacturing processes.