Vision
The CMOS era is breaking open
The semiconductor industry spent decades optimizing yield in a world dominated by high-volume, slowly evolving CMOS manufacturing. The next era looks different.
Computing, power and interconnect bottlenecks, accelerated by the demands of AI data centers, are driving the rapid rise of photonics, quantum, MEMS, compound semiconductors and heterogeneous integration. These technologies demand a level of process innovation and manufacturing flexibility that legacy fabrication systems were never built to support.
The winners of this new era will not be defined by scale alone, but by how fast they can develop, integrate and scale new technologies without sacrificing yield.
The bottleneck is increasingly the ability of manufacturing lines to adapt quickly to new requirements, materials and architectures, changing tool conditions, and yield excursions. When millions of dollars are at stake with every run, competitiveness depends on how well manufacturers can interpret thousands of signals, and identify what changed or needs to change to hit a target and rapidly adapt.
In short, intelligent and adaptive semiconductor manufacturing is becoming a critical competitive advantage.
The missing reinforcement loop for the physical world
Today’s AI models learn primarily from existing digital knowledge. The next frontier in manufacturing is learning continuously from the physical world.
Semiconductor fabrication generates an extraordinary stream of experiments, sensor data, metrology and manufacturing outcomes, but today much of that information remains fragmented, under-contextualized and difficult to turn into action.
FabuBlox is building the intelligence layer enabling closed-loop learning and AI-native semiconductor fabs. define to fabricate to measure to learn to adapt
By combining physics-informed models with traditional machine learning and agentic AI, grounded in the dynamic operating context of a fab, we build an adaptive and autonomously learning system. Combined with Bayesian DoE qualification, requalification, and technology-transfer cycles are drastically shortened.
Embedded in a unified data framework, the result is compounding process intelligence that grows with every wafer, rather than disappearing into charts, files and individual experience.
Toward autonomous semiconductor labs and fabs
FabuBlox is not only building an AI layer for intelligent fabrication. It is building the orchestration layer for autonomous fabrication.
In this future, AI does not sit beside the fab as another analytics tool. Fleets of specialized agents, spanning areas such as lithography, deposition and etching, participate directly in the manufacturing loop, from materials and process discovery through integration, scale-up, technology transfer and manufacturing co-optimization.
While engineers define objectives and retain control of critical decisions, agents design the next run, interpret signals, coordinate interdependent results, and adapt processes to real-time fab conditions. Most importantly, they continuously learn from physical outcomes.
By orchestrating intelligence across processes, equipment and even facilities, FabuBlox will build fab-specific world models, enabling AI to drive process integration and optimization with increasing autonomy.