Tim Chan
About Tim Chan
Tim Chan is a Senior Principal R&D Engineer at ON Semiconductor, specializing in computer vision object detection models and ADC techniques for low power, high-speed sensor platforms. He previously worked at Intel for 24 years in various engineering roles and holds a Master of Philosophy in device physics from The University of Hong Kong.
Work at Onsemi
Tim Chan has been employed at ON Semiconductor as a Senior Principal R&D Engineer since 2016. In this role, he focuses on advancing technologies related to computer vision and object detection models. His work primarily utilizes the TensorFlow deep learning platform, specifically tailored for embedded applications. This position allows him to leverage his extensive experience in engineering and research to contribute to innovative solutions in the semiconductor industry.
Previous Experience at Intel
Tim Chan worked at Intel for a total of 13 years, from 1990 to 2003, and then again from 2003 to 2014. Initially, he served as a Staff Design Engineer, where he contributed to various design projects. Later, he advanced to the role of Principal Design Engineer/Manager, overseeing design teams and projects in Santa Clara, California. His tenure at Intel provided him with significant experience in design engineering and management within the technology sector.
Education and Expertise
Tim Chan holds a Master of Philosophy in Electrical and Electronics Engineering with a focus on device physics from The University of Hong Kong. He also earned a Bachelor of Science degree in the same field from the same institution. His educational background has equipped him with a strong foundation in engineering principles, which he applies in his current research and development work, particularly in low power high-speed sensor platforms and ADC techniques.
Specialization in Computer Vision
Tim Chan specializes in developing computer vision object detection models, utilizing the TensorFlow deep learning platform. His expertise extends to embedded applications, where he focuses on creating efficient models that can operate effectively within constrained environments. This specialization reflects his commitment to advancing technologies that enhance machine perception capabilities.