Jay Prakash Pathak

Title: Innovations of Jay Prakash Pathak

Introduction

Jay Prakash Pathak is an accomplished inventor based in Pleasanton, CA (US). He holds a total of 6 patents that showcase his expertise in the field of semiconductor technology and machine learning. His innovative contributions have significantly advanced the capabilities of semiconductor design and analysis.

Latest Patents

One of his latest patents is titled "Methods and systems for predicting silicon density for a metal layer of semi-conductor chip via machine learning." This invention involves receiving a specification for a semiconductor chip that includes a set of photomasks associated with a metal layer. The process identifies multiple portions of the metal layer area and generates respective images based on the photomasks. A trained machine learning model is then used to predict the silicon density of metal wires for each portion, ultimately calculating an average silicon density for the entire area.

Another notable patent is the "Machine-learning based solver of coupled-partial differential equations." This invention addresses the simulation of physical systems by utilizing a solver trained with generative neural networks. The solver operates in a latent vector space, allowing for the partial decoupling of variables while efficiently solving partial differential equations.

Career Highlights

Jay Prakash Pathak is currently employed at Ansys, Inc., where he applies his innovative skills to develop advanced solutions in the semiconductor industry. His work focuses on integrating machine learning techniques to enhance the accuracy and efficiency of semiconductor design processes.

Collaborations

Throughout his career, Jay has collaborated with notable colleagues, including Wen-Tze Chuang and Norman H. Chang. These partnerships have fostered a creative environment that encourages the exchange of ideas and the development of groundbreaking technologies.

Conclusion

Jay Prakash Pathak's contributions to the field of semiconductor technology and machine learning exemplify his commitment to innovation. His patents reflect a deep understanding of complex systems and a drive to improve existing methodologies.