In-Depth Knowledge in Cutting-Edge Optical Technology
Optical technology is a core technology in the lithography process, which precisely transfers nanometer-scale patterns onto silicon wafers in semiconductor manufacturing.
Current lithography technology is an integration of numerous cutting-edge technologies and know-how, including technologies to transfer patterns that exceed the diffraction limit (the fundamental limitation of processing or reproducing structures smaller than the wavelength of light), such as those for "shorter exposure wavelength," "higher numerical aperture optical systems," "super-resolution" using modified illumination or phase-shift masks, and "optical proximity correction" for design patterns, and inverse lithography technology (ILT) to derive the mask shape inversely from the desired pattern.
In terms of technology development for "shorter exposure wavelengths, Japan is globally competitive in EUV mask defect inspection equipment and EUV-related materials, while it lags behind other countries in cutting-edge EUV exposure equipment". In order to catch up to the progress in EUV technology in general, the Japanese government is supporting R&D through assistance to research institutions and companies.
ALITECS has been active in the cutting-edge lithography field, with members who have played key roles in technology development in the sector. Since 2021, the company has been entrusted by the Ministry of Economy, Trade and Industry (METI) to implement the project to strengthen the management system of important technologies (feasibility study on the establishment of a supply chain system in Japan for edge semiconductor manufacturing involving ultra-fine processing)." together with Gigaphoton Inc., Canon Inc., and Nikon Corporation.
We also have a track record in lithography simulations using highly accurate optical models that are compatible with semiconductor masks with 3D structures.
AI Technology Utilization
ALITECS achieves high-precision, high-efficiency inspection technology with its in-depth knowledge of the characteristics of both physical and AI models and optimally utilizing them.
We use models based on the laws of physics for light propagation and interference in optical simulations, as well as AI-based methods to optimize complex parameters. We deliver high accuracy and efficiency simulations by leveraging the "theory-based reproducibility" enabled by physical models and the "excellent predictions in complex systems made possible by learning from large amounts of data" by AI models.
In addition, we have employed an AI-based image recognition technology that utilizes deep learning to automatically classify defects in product inspections. Rather than simply using AI, we are working to achieve higher accuracy by examining categories based on our extensive knowledge of optics and inspection.