Author: Segen Sara A. Habte

Author: Gilad Nave

Date: September 13^th, 2023 at 1:00pm

Zoom Link: https://umd.zoom.us/j/5095422567

Location: EGR-2164, Martin Hall

Committee Members:

Dr. Francis Patrick McCluskey, Chair

Dr. Mohammad Al-Sheikhly, Dean’s Representative

Dr. Hugh Bruck

Dr. Diganta Das

Dr. Abhijit Dasgupta

Dr. Peter Sandborn

Title: Electrical and Structural Formation of Transient Liquid Phase Sinter (TLPS) Materials During Early Processing Stage

Abstract:

The growing demands of electrification are driving research into new electronic materials. These electronic materials must have high electrical conductivity, withstand harsh environments and high temperatures and demonstrate reliable solutions as part of complete electronic packaging solutions. This dissertation focuses on characterizing the initial stage of the manufacturing process of Transient Liquid Phase Sinter (TLPS) alloys in a paste form as candidates for Pb-free high-temperature and high-power electronic materials.

The main objective of this dissertation work is to investigate the factors and decouple the multiple cross effects occurring during the first stage of TLPS processing in order to improve the understanding of material evolution. The work proposes, develops, and conducts in-situ electrical resistivity tests to directly measure material properties and analyze the dynamics at different stages of the material’s evolution. The research explores various factors, including alloying elements, organic binders, and heating rates, to understand their effects on the development of electrical performance in electronic materials. More specifically, the work examines the performance of Ag-In, Ag-Sn and Cu-Sn TLPS paste systems. Additionally, packing density and changes in cross-section are investigated using imaging techniques and image processing to gain insights into the early formation of the material’s structural backbone. An Arrhenius relationship together with Linear Mixed Models (LMM) techniques are used to extract the activation energies involved with each of the processing stages. The study then develops procedures to model different states of the TLPS microstructures at different heating stages based on experimentally observed data. Using these models, the study uses Finite Element Method (FEM) analysis to verify the experimental results and gain a better understanding and visualization into the involved mechanisms. This investigation not only sheds light on the material’s behavior but also has implications for robust additive manufacturing (AM) applications.

Author: Lokesh Sangepu

Date: Friday, July 28th, 2023, at 11:00 AM

Zoom Link:https://umd.zoom.us/meeting/register/tJYocuyprjwtHNdboK5kJ-r6O3ua_2WlLi3_

Location: EGR-2164, Martin Hall

Committee Members:

• Dr. Diganta Das, Chair
• Prof. Francis Patrick McCluskey
• Prof. Peter Sandborn

Thesis Title: PART SELECTION AND MANAGEMENT BASED ON RELIABILITY ASSESSMENT FOR DIE-LEVEL FAILURE MECHANISMS

Abstract:

Electronic part manufacturers often communicate part reliability information using metrics such as mean time between failures (MTBF) or failure per billion hours (FIT). However, these metrics, which rely on constant failure rate assumptions, do not adequately account for damage accumulation or wear-out phenomena leading to limitations in making informed decisions regarding the part selection and management for specific applications. This thesis addresses these limitations by proposing a physics-of-failure approach for developing a part selection methodology based on time-to-failure estimation of electronic parts.

The thesis contributes to the field by providing a comprehensive and physics-based approach to perform part selection and management. By moving beyond constant failure rate assumptions and considering wear-out phenomena, it offers a more accurate estimation of time to failure for electronic parts. The thesis begins by providing the challenges associated with manufacturers’ avoidance of sharing critical information, highlighting the impact on product quality, reliability, safety, and customer satisfaction. It describes that the insufficient information manufacturers provide hampers decision-making processes, necessitating an alternative approach for part selection.

The thesis focuses on four die-level failure mechanisms and investigates the extent to which industry-published documents discuss these mechanisms and their applicability to failure models. By understanding the specific failure mechanisms, the thesis aims to assist in selecting an appropriate failure model concerning the part and identify the required parameters for estimating the part’s time to failure. A methodology is developed to perform part selection utilizing the estimated time to failure. An application is created using MATLAB GUI to facilitate practical implementation, enabling designers, engineers, and procurement teams to make informed decisions when selecting electronic parts for specific applications. The methodology considers the susceptibility of parts to die-level failure mechanisms and identifies components with superior reliability performance. This approach enables informed decision-making, enhances product reliability, and improves customer satisfaction. The research findings and methodology presented in this thesis provide valuable insights for users to improve the reliability and performance of electronic systems through effective part selection.

Author: Eesh Kamrah

Date: Tuesday, July 25th, 2023, at 11:00 am

Location: EGR-2164

Committee Members:

• Dr. Mark Fuge / Advisor
• Dr. Shapour Azarm
• Dr. Nikhil Chopra

Title of Thesis: EFFECTS OF DIVERSE INITIALIZATION ON BAYESIAN OPTIMIZERS.

Abstract: Design researchers have struggled to produce quantitative predictions for exactly why and when diversity might help or hinder design search efforts.

This thesis addresses that problem by studying one ubiquitously used search strategy – Bayesian Optimization (BO) – on a 2D test problem with modifiable convexity and difficulty.
Specifically, we test how providing diverse versus non-diverse initial samples to BO affects its performance during search and introduce a fast ranked-DPP method for computing diverse sets, which we need to detect sets of highly diverse or non-diverse initial samples.

We initially found, to our surprise, that diversity did not appear to affect BO, neither helping nor hurting the optimizer’s convergence. However, follow-on experiments illuminated a key trade-off. Non-diverse initial samples hastened posterior convergence for the underlying model hyper-parameters a Model Building advantage. In contrast, diverse initial samples accelerated exploring the function itself a Space Exploration advantage. Both advantages help BO, but in different ways, and the initial sample diversity directly modulates how BO trades those advantages. Indeed, we show that fixing the BO hyper-parameters removes the Model Building advantage, causing diverse initial samples to always outperform models trained with non-diverse samples.
These findings shed light on why, at least for BO-type optimizers, the use of diversity has mixed effects and cautions against the ubiquitous use of space-filling initializations in BO.
To the extent that humans use explore-exploit search strategies similar to BO, our results provide a testable conjecture for why and when diversity may affect human-subject or design team experiments.

The thesis is organized as follows: Chapter 2 provides an overview of existing studies that explore the impact of different initial stimuli. In Chapter 3, we explain the methodology used in the subsequent experiments. Chapter 4 presents the results of our initial study on the diverse initialization of BO (Bayesian Optimization) applied to the wildcat wells function. In Chapter 5, we analyze the conditions under which less diverse initial examples perform better and expand on these findings in Chapter 6 by considering additional ND continuous functions. The final chapter discusses the limitations of our findings and proposes potential areas for future research.