AUSTIN, TX – November 7, 2023 – SandBox Semiconductor, a pioneer in making use of physics-based, AI-enabled modeling to speed up the event of semiconductor manufacturing processes, is ready to make waves on the upcoming 2023 AVS Worldwide Symposium and Exhibition. The occasion, going down from November 5-10, 2023 on the Oregon Conference Heart in Portland, OR, will function a platform for Dr. Sebastian Naranjo, Computational Engineer at SandBox Semiconductor, to make clear the fast optimization of gap-fill recipes utilizing machine studying.
Semiconductor manufacturing is a posh course of, involving as much as 1,200 steps. Historically, course of growth has been carried out sequentially, with every unit course of examined separately. Nonetheless, SandBox Semiconductor is pushing the boundaries of conventional approaches by combining physics-based modeling with AI. This progressive method permits engineers to streamline the method growth, decreasing timelines and minimizing reliance on bodily experiments.
Dr. Naranjo’s presentation on Thursday, November 9, 2023, at 3:40 pm PT, will delve into the thrilling potentialities that machine studying presents for course of engineers. By leveraging machine studying algorithms, engineers can co-optimize a number of unit processes concurrently, considerably decreasing prices, time, and complexity related to recipe growth. The main target of the presentation will focus on a gap-fill course of circulation, a vital part in semiconductor manufacturing.
“The truth that AVS has devoted a full session to AI and machine studying speaks volumes concerning the growing significance of know-how within the semiconductor manufacturing course of,” stated Dr. Meghali Chopra, SandBox Semiconductor CEO & Co-founder. “Our workforce is worked up to showcase how AI could be utilized to recipe optimization for deposition, in the end accelerating course of growth, expediting time-to-market for brand new merchandise, and driving down general prices.”
SandBox Semiconductor, based in 2016, has cemented its place as a pacesetter in AI-based software program for semiconductor course of growth. The corporate’s built-in no-code AI instrument suite empowers course of engineers to construct physics-based, AI-enabled fashions to deal with challenges all through the event, ramp-up, and high-volume manufacturing phases.
Utilizing SandBox’s cutting-edge physics-based fashions and machine studying instruments, engineers can simulate, predict, and measure course of outcomes in a digital setting. With even restricted units of experimental information, SandBox’s instruments can extract beneficial insights and patterns, giving engineers a deeper understanding of producing processes and enabling knowledgeable selections about recipe changes.
SandBox Semiconductor’s experience in numerical modeling, machine studying, and manufacturing optimization has earned them partnerships with main chip producers and semiconductor gear suppliers. Headquartered in Austin, Texas, SandBox Semiconductor continues to drive innovation throughout the semiconductor trade.
FAQ
What’s SandBox Semiconductor?
SandBox Semiconductor is a pioneer in using physics-based, AI-enabled modeling to speed up the event of semiconductor manufacturing processes. Their AI-based software program empowers course of engineers with instruments to construct their very own fashions and clear up challenges all through the manufacturing course of.
How does SandBox Semiconductor’s AI instrument suite work?
SandBox’s built-in no-code AI instrument suite combines physics-based fashions and machine studying instruments. This permits course of engineers to just about simulate, predict, and measure course of outcomes, even with restricted experimental information. The instruments extract insights and patterns to help engineers in making knowledgeable selections about recipe changes.
What will likely be mentioned on the AVS presentation?
The AVS presentation by Dr. Sebastian Naranjo will give attention to the fast optimization of gap-fill recipes utilizing machine studying. It can discover the probabilities of co-optimizing a number of unit processes concurrently, decreasing prices, time, and complexity related to recipe growth in semiconductor manufacturing.
