Emerging reports have it that London-based Flexciton raised €17.8M for its optimization technology ambition aimed at improved efficiency and productivity of semiconductor manufacturing processes. The subject company is believed to have developed a unique solution that analyses the real-time data. However, the Series A funding round was led by Nadav Rosenberg (Saras Capital).
The company (Flexciton) which is headquartered in London was founded in 2016, whose solution-based services focus on in-depth real-time data that every manufacturing plant generates with the aid of cutting edge technology to determine the right action that needs to be taken to optimize production.
Flexciton was founded by ‘Jamie Potter’ and ‘Dennis Xenos’ who actually worked in the optimized manufacturing field for over ten years while focusing on how advanced mathematics can solve manufacturing scheduling concerns. In the process, they figure out that it was impossible for humans to understand the trillions of options that are generated by manufacturing processes while noting that it was only through the application of mathematics that the unpredictability of such complex systems could be understood, EU Startups have learned.
It’s evident, semiconductors are really complex to develop, as such, it’s worth noting that modern semiconductor manufacturing plants are the most complex manufacturing environment in the universe, with the production process generating more scheduling options than there are atoms in the world. For instance, a next-generation chip wafer could potentially go through between 500-2,000 machine steps in a dynamic process. However, according to analysts, it’s been figured out that the end-to-end process of producing a single chip may take six months to a year to complete. In view of this, the CEO and co-founder of Flexciton ‘Jamie Potter’ stated:
“Automation is already used by many semiconductor manufacturers. However, even in advanced fabs where scheduling itself is automated, the software used to make these decisions tends to be based on predefined rules programmed by humans and determined by historical data. Yes, it can calculate different options far quicker than a human operator could, but the options are still ‘best guesses’ rather than optimal outcomes”.
“Flexciton is able to create an overview of how the entire fab is operating and rapidly sift through the trillions of options available, to come up with the optimal decision at the precise point in time. Using AI-powered mathematical algorithms and Mixed Integer Linear Programming, we can analyze real-time data – not historical – and make the best choices possible based on what is happening in a fab at a given moment. Our vision is to become the best in the world at running semiconductor fabs, before turning our attention to support other manufacturers. This investment play a key part in achieving this, as we expand our team”.
As for the startup’s service, it’s understood to have delivered efficiency gains of 10%. With this calculated advantage, a manufacturing plant (fab) using 1,000 machines, for instance, would mean saving such factory tens of millions per year. Looking at this from a broader perspective, it will be fair to know that there are currently more than 1,000,000 machines globally, yet to be optimized which continues to grow in number due to demand for semiconductors.
The lead investor of the current Series A round ‘Nadav Rosenberg’ stated: “Semiconductors are the fundamental building blocks of modern life. More than any other development of the scientific age, they have completely revolutionized the way that we work, play, communicate and learn. Demand will keep growing, and the industry must consider how to better utilize its current assets, before expanding and using further resources to meet demand. Efficiency is key. Flexciton is the first company to successfully apply this level of machine intelligence to real-world manufacturing, with hugely impressive results”.
The latest fundraise will be used for hiring across the team and further expansion. However, the company’s team has since grown to 40, with combined disciplines of mathematics, semiconductor scheduling, optimization, data science, and software development.
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