materialsIN

materialsIN's proprietary methodology automates, and curates, customers' data, even when said data is sparse and fragmented.

The methodology deploys hierarchical knowledge extraction frameworks, which enables quantitative evaluation.

The end result for customers are results that are quick, precise, and accurate.

materialsIN has partnered with BQP to bring quantum-classical machine learning (QML) to the semiconductor industry.

Market Problem:
> Semiconductor manufacturers are under growing pressure to improve yield and reliability while working with increasingly complex materials, tighter process windows, and heightened demands for speed, cost-efficiency, and environmental sustainability

Solution:
> The materialsIN - BQP partnership addresses these challenges by combining BQP’s hybrid quantum-classical machine learning (QML) with materialsIN’s advanced informatics and domain expertise.

Key Results:
> Higher precision, reduced trial and errors, energy and cost savings, shorter design cycles