School of Engineering Department of Mechanical and Aerospace Engineering 171 Smart Contact Lens Supervisor: LAM David Chuen Chun / MAE Student: POYYAMOZHI Sricharan / CBME Course: UROP1100, Fall This paper presents a methodology to analyze the chemical solutions used during PCB fabrication after photolithography. The main baths, developing solution, etch bath and strip bath along with their rinse cycles are isolated and analyzed using Ultra-Violet Spectroscopy. The finished coils are also checked for defects using Scanning Electron Microscopy. The data collected with these methods are processed using matlab to give simple contamination and quality gauges. The UV-Spectroscopy was found to be exemplary other than when analyzing etching solution which has transition metal ions resonating across a wide spectrum. SEM was found to be a capable identifier for streaking but inefficient for whisker identification. Smart Contact Lens Supervisor: LAM David Chuen Chun / MAE Student: POYYAMOZHI Sricharan / CBME Course: UROP2100, Spring In this paper major defects that are common during Printed Circuit Board Manufacturing and their causes are introduced. Process optimization during initial fabrication during photolithography and photoresist development are considered to reduce error. The defects, with respect to specific chemical baths are considered and monitoring protocols are established to identify dirtiness thresholds. The monitoring of etching baths are fully determined. Whisker formation is characterized to additive PCB fabrication and a basis for future electroplating experiments is established. A new circuit design is introduced to uniquely induce and research whiskers by concentrating electricity at sharp turns and research the effects of scale on whisker formation. Application of AI-based Technique to Enhance Thermal Comfort Sensing for Smart Air Conditioner Supervisor: LEE Yi-Kuen / MAE Student: ZHENG Tianshi / COGBM Course: UROP1100, Spring In this project, we need to make improvements based on the previous system, which combines the Bluetooth connection with Arduino and Web database sending using Android App. The app collects data from the mobile phone’s built-in sensors, and receives other parameters through Bluetooth fromArduinoMCU, finally compute the output of the PMV value. During this semester, I developed an iOS app that has the similar functions to the previous Android one using XCode and Swift language.