Fractilia announces a new technology roadmap, providing a breakthrough in solving the challenges of yield loss and production delays in advanced semiconductor processes.

On the 16th, Practilia held a press conference, and CEO Edward Charrier, who recently visited Korea, and CTO Chris A. Mack, a world authority in the field of stochastic lithography, announced for the first time the results of Practilia's latest research to solve the 'stochastic mutation' problem.

This strategy focuses on overcoming 'stochastic variability', a random variability, through precise measurement and probability-based control to promote the transition to high-yield mass production.

According to Practilia, the stochastic resolution gap in current extreme ultraviolet (EUV) processes is approximately 5 nanometers.

This refers to the difference between the feature sizes that can be implemented in the R&D stage and the yield standards that can be achieved in actual mass production, and it arises from random patterning variability.

Global semiconductor manufacturers are experiencing losses in the hundreds of billions of won in the latest process nodes, which is considered a key issue hindering high-volume manufacturing (HVM). To overcome this volatility, Practilia has proposed three strategies: △securing ultra-precision stochastic measurement technology, △introducing a probability-based process control method, and △innovating design and materials that take stochastic effects into account.

Chris Mack, CTO of Practilia, said, “Stochastic variation is a structural problem that cannot be solved with existing process control methods. High-yield, high-reliability mass production is possible only when the design and process itself are redefined through probability analysis,” and diagnosed that “the overall growth rate of the electronics industry is limited by stochasticity.”

In the past, stochastic variation had a minimal impact on yield because it was smaller than the critical feature size, but with the introduction of high-NA EUV processes and improved resolution, variability has become a major source of process error.

Accordingly, the stochastic gap has emerged as an essential problem that must be solved in advanced semiconductor manufacturing.

Practilia said the stochastic gap could be reduced through new design approaches, material selection, and integration of process technologies.

In particular, it was emphasized that measurement-based solutions are key to predicting and preventing stochastic defects.