Working on the design of tool management system for NYU NanoFab

Designed a 4-layer PCB testing board for a 256-pin analog IC dedicated to dopamine concentration measurement. This involved careful planning and precision in the sourcing and soldering of SMD components, both manually and via a reflow oven, to ensure accurate assembly.

To facilitate versatile signal control and testing, I integrated an MSP430 microcontroller and IO expanders for GPIO expansion. This setup enabled the independent testing of eight FSCV channels, with analog switches controlled by the MCU for precise signal regulation.

Engineering the PCB to generate stable voltages (-1.65V, +1.65V, +5V, -5V, and 3.3V) was crucial for maintaining low-noise tolerant and reliable testing conditions. Additionally, I designed a GUI to control the board using the UART interface of the MCU, enhancing user interaction and control.

I also created a NIDAQ interface from scratch using Python, employing multithreading and multiprocessing techniques to ensure smooth operation and flexible data acquisition. Various tests were conducted to confirm that the IC’s functionality and performance met the specified requirements.

Lastly, I demonstrated the effectiveness of a high-pass filter in mitigating sensor drift, thereby enhancing the reliability and accuracy of the dopamine concentration measurements.

I led the migration of EZFAP from VBA to G-suite, optimizing the tool that supports the FAP development process and reducing processing time by two hours per day. Additionally, I executed over 100 test cases for avionics software, ensuring compliance with industry standards and improving reliability, which resulted in a 15% decrease in software errors. I also collaborated with cross-functional teams, honing my verbal communication skills and enhancing teamwork, contributing to a 10% reduction in project turnaround time.