Company Filing History:
Years Active: 2022-2024
Title: Innovations of Inventor Xing Lin
Introduction
Xing Lin is a prominent inventor based in Los Angeles, CA. He has made significant contributions to the field of optical-based machine learning, holding a total of 3 patents. His work focuses on developing advanced technologies that leverage diffractive deep neural networks for various applications.
Latest Patents
One of Xing Lin's latest patents involves devices and methods employing optical-based machine learning using diffractive deep neural networks. This all-optical Diffractive Deep Neural Network (DNN) architecture learns to implement various functions or tasks after a deep learning-based design of the passive diffractive or reflective substrate layers. These layers work collectively to perform the desired function or task. The architecture was successfully confirmed experimentally by creating 3D-printed DNNs that learned to implement handwritten classifications and lens functions at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement. It will find applications in all-optical image analysis, feature detection, and object classification, enabling new camera designs and optical components that can learn to perform unique tasks using DNNs. In alternative embodiments, the all-optical DNN is used as a front-end in conjunction with a trained digital neural network back-end.
Career Highlights
Xing Lin is affiliated with the University of California, where he continues to push the boundaries of innovation in optical technologies. His research has garnered attention for its potential to revolutionize how optical systems are designed and utilized.
Collaborations
Xing Lin has collaborated with notable colleagues, including Aydogan Ozcan and Yair Rivenson. Their combined expertise has contributed to advancing the field of optical machine learning.
Conclusion
Xing Lin's innovative work in optical-based machine learning and diffractive deep neural networks positions him as a leading figure in his field. His contributions are paving the way for future advancements in optical technologies.