Software : Data & Analytics : Semiconductor
proteanTecs was founded with a mission to enable the electronics industry to continue to scale. As veterans of the industry, the founders witnessed firsthand the rising challenge of achieving quality and reliability without affecting cost and performance, especially as technologies advanced. Existing value chain strategies and in-field operating models were long due for a digital revolution. Since then, the company has introduced a breakthrough approach to overcome the many challenges that come with scale. Redefining industry metrics and benchmarks, we’re raising the bar to meet future demands. proteanTecs is now trusted by key customers across various industries and is set to lead the revolution. Founded in 2017, the company is headquartered in Israel with offices in in New Jersey, California and Taiwan.
ProteanTecs strengthens its AI-enabled chip analytics platform, raises $45M
ProteanTecs, an Israel-based company providing deep data solutions for electronics health and performance monitoring, today announced it raised $45 million. ProteanTecs claims it’s built advanced cloud and edge enterprise solutions that monitor the health and performance of chips powering electronics from design to field.
ProteanTecs says this additional capital will help to accelerate its market reach by expanding its global team, enabling the company to continue to innovate and enhance its product offering. The company also claims it will mark a significant milestone in the company’s growth strategy and builds on its growth equity round. The funding round was solely led by Addition. ProteanTecs was founded in 2017 and has raised almost $200 million since inception.
Big Payback For Combining Different Types Of Fab Data
Collecting and combining diverse data types from different manufacturing processes can play a significant role in improving semiconductor yield, quality, and reliability, but making that happen requires integrating deep domain expertise from various different process steps and sifting through huge volumes of data scattered across a global supply chain.
AI In Inspection, Metrology, And Test
“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”
That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”
Early And Fine Virtual Binning
ProteanTecs enables manufacturers to bin chips virtually, in a straightforward and inexpensive way based on Deep Data. By using a combination of tiny on-chip test circuits called “Agents” and sophisticated AI software, chip makers can find relationships between any chip’s internal behavior and the parameters measured during the standard characterization process. Those relationships can be used to measure similar chips’ internal characteristics at wafer sort to precisely predict how chips would perform during Final Test, even before the wafer is scribed.
Early And Fine Virtual Binning
ProteanTecs, which provides an AI platform to monitor chip reliability, today closed a $45 million funding round. The company says the fresh capital will bolster its go-to-market strategy and operations as it seeks to scale worldwide.
Chip design and manufacturing is a high-risk, high-reward pursuit. Mistakes made during the earliest phases are often enormously costly — chip fabrication plants cost billions to build. And the most sophisticated hardware can take years to hammer out, with intense speculation about how to optimize the next generation for workloads that might come into vogue.