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 strains in actual time.
It has raised $8.5 million in Sequence A bringing its whole 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 elementary problem in semiconductor manufacturing: changing uncooked manufacturing knowledge, from defect pictures to tools indicators, 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 by way of her expertise constructing automation options for producers like Hyundai Motors and GE and led product growth at startups like Embibe. Agarwal provides technical expertise from her time at Visa, the place she constructed large-scale knowledge analytics programs, a few of which had been later protected as commerce secrets and techniques. A talented coder with a robust background in arithmetic, she had lengthy been concerned with making use of AI to conventional industries past fintech.
Collectively, the duo evaluated sectors from aviation to automotive earlier than touchdown on semiconductors. Regardless of the semiconductor business’s popularity for precision, inspection processes stay largely handbook and fragmented, Agarwal informed TechCrunch. After talking with greater than 50 engineers, it grew to become clear there’s vital room to modernize how high quality checks are achieved, she added.
Fabs as we speak are stuffed with dashboards, SPC charts, and inline inspection programs, however most solely show knowledge with out additional evaluation, Agarwal mentioned. “The burden of using it for decision-making still falls on engineers: [they must] spot patterns, investigate anomalies, and trace root causes. That’s time-consuming, subjective, and doesn’t scale well with increasing process complexity.”
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SixSense gives engineers with early warnings to deal with potential points earlier than they escalate with capabilities comparable to defect detection, root trigger evaluation, and failure prediction.
SixSense’s platform can be particularly designed for use by course of engineers relatively than knowledge scientists, Agarwal mentioned. “Process engineers can fine-tune models using their own fab data, deploy them in under two days, and trust the results — all without writing a single line of code. That’s what makes the platform both powerful and practical.”
The aggressive panorama contains in-house engineering groups utilizing instruments like Cognex and Halcon, inspection tools makers integrating AI into their programs, 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 to this point. Clients have reported as much as 30% sooner manufacturing cycles, a 1-2% enhance in yield, and a 90% discount in handbook inspection work, the founders mentioned. The system is appropriate with inspection tools that covers over 60% of the worldwide market.
“Our target customers are large-scale chipmakers — including foundries, outsourced semiconductor assembly and test providers (OSATs), and integrated device manufacturers (IDMs),” Agarwal mentioned. “We’re already working with fabs in Singapore, Malaysia, Taiwan, and Israel, and are now expanding 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 expand aggressively in Malaysia, Singapore, Vietnam, India, and the U.S. — and that’s a tailwind for us. Why? Because we’re already based in the region, and many of these new facilities are starting fresh — without legacy systems weighing them down. That makes them far more open to AI-native approaches like ours from day one,” Agarwal informed TechCrunch.
