● Set up and test semiconductor devices, collected data across multiple parameters and temperatures, and analyzed performance using dataConductor and Excel.● Conduct reliability tests using motherboards and daughter cards to ensure product performance under extreme conditions.● Collaborate with team to analyze data, identify areas for improvement, and implement changes to enhance production efficiency.● Utilize application boards, oscilloscopes, multimeters, power supplies, and GUIs to communicate with devices and capture data accurately● Monitor inventory on possible misconfigured products by yield analysis.● Conduct wafer probing using specialized equipment.

○ Worked collaboratively in a team of 4 in developing a prototype device to recognize fruits/vegetables using TensorFlow machine learning algorithm with the assistance of PiCameras and RaspberryPis as well as multi-axis arms. Was able to get the machine learning and multi-axis arms working separately.■ Software Language: Python■ Software Application: Pycharm■ Researched what Tensorflow is, how it works, and how to apply to our project■ Gathered images of fruits/vegetables, fed into training program for recognition of color and shape■ Errors Faced: Too many images led to long periods of training and crashing of training program. Few images led to improper training and recognition failure. Tensorflow version had to be downgraded to run training program, changing program template to fit our requirements

Determined the initial purpose of computer use.Used pcpartpicker.com to gather compataibility and budget information among the parts,With the help of an Udemy's Online IT Course, was able to follow the steps to accomplish building the PCs.System 1: Intel I9-10850K, 3200MHz 32GB RAM, Asus TUF 3080ti GPU, 750W PSU, Asus ROG Strix Z590-A MotherboardSystem 2: Intel I7-9700K, 3200MHz 32GB RAM, Gigabyte 960 GPU, 750W PSU, Z170 Asus Motherboard