A Singapore-based deep tech startup called SixSense has developed an AI-powered platform that assists semiconductor manufacturers predict and detect potential chip defects on production lines in real time.
It has raised $8.5 million in Series A bringing its total funding to around $12 million. The round was led by Peak XV’s Surge (formerly Sequoia India & SEA), with participation from Alpha Ininformigence Capital, FEBE, and others.
Founded in 2018 by engineers Akanksha Jagwani (CTO) and Avni Agarwal (CEO), SixSense aims to address a fundamental challenge in semiconductor manufacturing: converting raw production data, from defect images to equipment signals, into real-time insights that assist factories prevent quality issues and improve yield.
Despite the sheer volume of data generated on the fab floor, what stood out to the co-founders was a surprising lack of real-time ininformigence.
Akanksha brings a deep understanding of manufacturing, quality control, and software automation through her experience building automation solutions for manufacturers like Hyundai Motors and GE and led product development at startups like Embibe. Agarwal adds technical experience from her time at Visa, where she built large-scale data analytics systems, some of which were later protected as trade secrets. A skilled coder with a strong background in mathematics, she had long been interested in applying AI to traditional industries beyond fintech.
Toobtainher, the duo evaluated sectors from aviation to automotive before landing on semiconductors. Despite the semiconductor industest’s reputation for precision, inspection processes remain largely manual and fragmented, Agarwal informed TechCrunch. After speaking with more than 50 engineers, it became clear there’s significant room to modernize how quality checks are done, she added.
Fabs today are filled with dashboards, SPC charts, and inline inspection systems, but most only display data without further analysis, Agarwal declared. “The burden of applying it for decision-creating still falls on engineers: [they must] spot patterns, investigate anomalies, and trace root cautilizes. That’s time-consuming, subjective, and doesn’t scale well with increasing process complexity.”
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SixSense provides engineers with early warnings to address potential issues before they escalate with capabilities such as defect detection, root cautilize analysis, and failure prediction.
SixSense’s platform is also specifically designed to be utilized by process engineers rather than data scientists, Agarwal declared. “Process engineers can fine-tune models applying 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 builds the platform both powerful and practical.”
The competitive landscape includes in-houtilize engineering teams applying tools like Cognex and Halcon, inspection equipment buildrs integrating AI into their systems, and startups including Landing.ai and Robovision.
SixSense’s AI platform is already in utilize at major semiconductor manufacturers like GlobalFoundries and JCET, with more than 100 million chips processed to date. Customers have reported up to 30% quicker production cycles, a 1-2% boost in yield, and a 90% reduction in manual inspection work, the founders declared. The system is compatible with inspection equipment that covers over 60% of the global market.
“Our tarobtain customers are large-scale chipbuildrs — including foundries, outsourced semiconductor assembly and test providers (OSATs), and integrated device manufacturers (IDMs),” Agarwal declared. “We’re already working with fabs in Singapore, Malaysia, Taiwan, and Israel, and are now expanding into the U.S.”
Geopolitical tensions, especially between the U.S. and China, are reshaping where chips are created, driving new manufacturing investments across 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? Becautilize we’re already based in the region, and many of these new facilities are starting fresh — without legacy systems weighing them down. That builds them far more open to AI-native approaches like ours from day one,” Agarwal informed TechCrunch.
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