A Singapore-based deep tech startup referred to as SixSense has developed an AI-powered platform that helps semiconductor producers predict and detect potential chip defects on manufacturing traces in actual time.
It has raised $8.5 million in Collection A bringing its complete funding to round $12 million. The spherical was led by Peak XV’s Surge (previously Sequoia India & SEA), with participation from Alpha Intelligence Capital, FEBE, and others.
Based in 2018 by engineers Akanksha Jagwani (CTO) and Avni Agarwal (CEO), SixSense goals to deal with a basic problem in semiconductor manufacturing: changing uncooked manufacturing knowledge, from defect photographs to tools alerts, into real-time insights that assist factories stop high quality points and enhance yield.
Regardless of the sheer quantity of information generated on the fab flooring, what stood out to the co-founders was a shocking lack of real-time intelligence.
Akanksha brings a deep understanding of producing, high quality management, and software program automation via her expertise constructing automation options for producers like Hyundai Motors and GE and led product improvement at startups like Embibe. Agarwal provides technical expertise from her time at Visa, the place she constructed large-scale knowledge analytics techniques, a few of which had been later protected as commerce secrets and techniques. A talented coder with a powerful background in arithmetic, she had lengthy been excited about making use of AI to conventional industries past fintech.
Picture Credit:SixSense
Collectively, the duo evaluated sectors from aviation to automotive earlier than touchdown on semiconductors. Regardless of the semiconductor trade’s fame for precision, inspection processes stay largely guide and fragmented, Agarwal instructed TechCrunch. After talking with greater than 50 engineers, it turned clear there’s vital room to modernize how high quality checks are achieved, she added.
Fabs immediately are full of dashboards, SPC charts, and inline inspection techniques, however most solely show knowledge with out additional evaluation, Agarwal mentioned. “The burden of utilizing it for decision-making nonetheless falls on engineers: (they need to) spot patterns, examine anomalies, and hint root causes. That’s time-consuming, subjective, and doesn’t scale properly with growing course of complexity.”
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SixSense supplies engineers with early warnings to deal with potential points earlier than they escalate with capabilities akin to defect detection, root trigger evaluation, and failure prediction.
SixSense’s platform can also be particularly designed for use by course of engineers reasonably than knowledge scientists, Agarwal mentioned. “Course of engineers can fine-tune fashions utilizing their very own fab knowledge, deploy them in below two days, and belief the outcomes — all with out writing a single line of code. That’s what makes the platform each highly effective and sensible.”
The aggressive panorama consists of in-house engineering groups utilizing instruments like Cognex and Halcon, inspection tools makers integrating AI into their techniques, and startups together with Touchdown.ai and Robovision.
SixSense’s AI platform is already in use at main semiconductor producers like GlobalFoundries and JCET, with greater than 100 million chips processed thus far. Prospects have reported as much as 30% sooner manufacturing cycles, a 1-2% increase in yield, and a 90% discount in guide inspection work, the founders mentioned. The system is appropriate with inspection tools that covers over 60% of the worldwide market.
“Our goal prospects are large-scale chipmakers — together with foundries, outsourced semiconductor meeting and take a look at suppliers (OSATs), and built-in system producers (IDMs),” Agarwal mentioned. “We’re already working with fabs in Singapore, Malaysia, Taiwan, and Israel, and are actually increasing into the U.S.”
Geopolitical tensions, particularly between the U.S. and China, are reshaping the place chips are made, driving new manufacturing investments throughout the globe.
“We’re seeing fabs and OSATs develop aggressively in Malaysia, Singapore, Vietnam, India, and the U.S. — and that’s a tailwind for us. Why? As a result of we’re already based mostly within the area, and lots of of those new amenities are beginning recent — with out legacy techniques weighing them down. That makes them much more open to AI-native approaches like ours from day one,” Agarwal instructed TechCrunch.
