A Singapore-based deep tech startup known 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 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 photographs to tools alerts, into real-time insights that assist factories stop high quality points and enhance yield.
Regardless of the sheer quantity of knowledge generated on the fab ground, 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 concerned about 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 trade’s popularity for precision, inspection processes stay largely handbook and fragmented, Agarwal instructed TechCrunch. After talking with greater than 50 engineers, it grew to become clear there’s important room to modernize how high quality checks are executed, she added.
Fabs at this time are stuffed with 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 must] spot patterns, examine anomalies, and hint root causes. That’s time-consuming, subjective, and doesn’t scale properly with rising course of complexity.”
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SixSense offers 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 be particularly designed for use by course of engineers moderately 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 so far. Prospects have reported as much as 30% quicker 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 goal clients are large-scale chipmakers — together with foundries, outsourced semiconductor meeting and check suppliers (OSATs), and built-in system producers (IDMs),” Agarwal mentioned. “We’re already working with fabs in Singapore, Malaysia, Taiwan, and Israel, and at the moment are 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 increase 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 primarily based within the area, and lots of of those new services are beginning contemporary — with out legacy techniques weighing them down. That makes them way more open to AI-native approaches like ours from day one,” Agarwal instructed TechCrunch.
