SixSense Raises $8.5M for AI Chip Defect Detection

SixSense, a Singapore-based deep tech startup, has just secured an $8.5 million Series A round led by Peak XV’s Surge, with participation from Alpha Intelligence Capital, FEBE and others. The funding brings its total raised to about $12 million and will accelerate deployment of its AI-powered platform for real-time semiconductor defect detection.

Real-Time Insights for Quality Control

Despite massive data streams on the fab floor—from defect images to equipment signals—most inspection systems only display charts and dashboards. The burden of spotting anomalies, tracing root causes, and tuning production still falls on process engineers. SixSense’s AI platform changes that by delivering early warnings, automated defect detection, and failure predictions in real time.

Founders Bridge Manufacturing and Analytics

Co-founders Avni Agarwal (CEO) and Akanksha Jagwani (CTO) bring complementary expertise. Jagwani built automation solutions for Hyundai Motors and GE and led product efforts at startups like Embibe. Agarwal, a former Visa data-analytics engineer, has architected large-scale systems now protected as trade secrets. Their combined backgrounds highlight the platform’s manufacturing and AI pedigree.

Strong Market Validation and Growth

• Over 100 million chips processed to date

• Up to 30% faster production cycles
• 1–2% boost in yield
• 90% reduction in manual inspection work

SixSense’s platform is already deployed at major chipmakers like GlobalFoundries and JCET. Process engineers can fine-tune models on their own fab data, deploy in under two days, and trust the insights without writing code—bridging the gap between AI innovation and shop-floor practicality.

Strategic Expansion in a Shifting Landscape

With fabs expanding across the U.S., Malaysia, Singapore, Vietnam, India and Israel, and new facilities opting for AI-native systems over legacy tools, SixSense is well positioned for global growth. Geopolitical tensions are reshaping manufacturing hubs, creating a tailwind for AI-driven quality control at every step of chip production.