Practilia helps high-volume manufacturing fabs identify potential process issues in minutes and provides support for EUV pattern control and yield improvement.

Fractilia, a next-generation semiconductor industry leader driving stochastic-based analysis and control, today announced the addition of the Fractilia Automated Measurement Environment (FAME™) portfolio’s latest product, the FAME 300.

Designed specifically for use in high-volume manufacturing (HVM) fab environments, the FAME 300 can measure, detect and monitor stochastic effects in real time, a major cause of patterning errors in advanced node processes.

With FAME 300, you can identify potential process issues caused by stochastic variation in your fab in just minutes, enabling rapid corrective action to improve control of your patterning process and optimize yield.

The FAME 300 utilizes Fractilia’s patented FILM™ (Fractilia Inverse Linescan Model) technology.

This technology is the only fab solution that can measure a variety of stochastic effects, including roughness, as well as critical dimension (CD) measurements, with high accuracy and precision.

Fractilia’s products have already been proven in R&D and process development environments for semiconductor manufacturers, equipment companies and semiconductor raw material suppliers. All measurements performed using Fractilia products can be transferred from the R&D stage to the HVM stage and can be fully automated.

“As the semiconductor industry continues to scale to sustain Moore’s Law, leading fabs are experiencing significantly increased stochastic variation that only gets worse,” said Chris Mack, CTO of Fractilia. “Accurate stochastic measurement is needed to control new fab processes to ensure they deliver good yields.” “By integrating the FILM measurement engine into our new high-throughput, reliable, low-latency FAME 300 platform, Fractilia is bringing stochastic analysis capabilities to the fab HVM environment. Fractilia customers can now better control their HVM processes and save months of time troubleshooting stochastic issues in their products.”