Use Efficient Data Collection Tools
Increase the performance of semiconductors by leveraging data sets from literature and theoretical repositories. Receive curated sets, insert them into initial machine learning (ML) recommendation models, fabricate semiconductor materials based on the experimental recommendations, and finally, characterize them. Use an ML feedback loop that allows for an efficient and supervised improvement of semiconductor materials.
Accelerate Semiconductor Research Using Machine Learning
Benefit from a unique and innovative approach to discovering and improving semiconductor materials. It relies on a flow that connects four critical research aspects: Material properties, preparation properties, and function properties. Find connections between these aspects and make predictions.
Connect, Standardize and Visualize Complex Semiconductor Data
Connect fabrication, testing, and characterization instruments of semiconductor materials used in academic laboratories, industrial laboratories, and production lines to the online platform. Tag, parse, standardize, and visualize complex data sets from the instruments and securely access them from any device.