PerformanceStar
PerformanceStar is a contract R&D company in Santa Clara, CA, specializing in high-performance big-data analysis and machine learning technologies for the semiconductor industry.
Company Overview
PerformanceStar is a contract R&D company based in Santa Clara, CA, focusing on developing high-performance big-data analysis and machine learning technologies for the semiconductor industry. Recognized for building R&D prototypes, the company converts these into mission-critical applications for its customers. PerformanceStar’s contributions help solve complex industry problems through its state-of-the-art machine learning applications.
Services
PerformanceStar specializes in creating advanced smart machine learning applications. Utilizing technologies like evolutionary computing, clustering, support vector machines, decision trees, Bayes’ nets, and more, the company addresses fundamental issues in semiconductor manufacturing. Services include diagnosing and correcting equipment performance problems, optimizing overall performance, and monitoring sensor streams in real-time using their Equipment Health Monitor (EHM) tool.
Meta-Learning Project
PerformanceStar's Meta-Learning project aims to automate significant parts of the machine learning configuration and usage processes. This initiative helps enhance the efficiency and effectiveness of implementing machine learning solutions, simplifying complex setups for diverse applications in the semiconductor industry and beyond.
Equipment Health Monitor (EHM)
The Equipment Health Monitor (EHM) is a proprietary tool developed by PerformanceStar for real-time monitoring of arbitrary sensor streams. This tool is crucial in semiconductor manufacturing, where it is deployed to monitor hundreds of sensors to detect unexpected behaviors and ensure optimal equipment performance.
Stem Cell Research Technologies
PerformanceStar also engages in computer vision and analytics for stem cell production. Their focus includes analyzing induced pluripotent stem cells for applications in fundamental research, disease treatment, tissue and organ replacement, and personalized drug testing. The company employs machine learning techniques such as regression, classification, and reinforcement learning to assess stem cell quality and differentiation capabilities, mining large datasets including enzyme, metabolome, and image data.