
Photoresist coating is the first critical step in wafer patterning, any variation in film thickness, streaks, or contamination can cause pattern distortion or complete lithography failure. Yet, traditional inline checks or manual inspections often miss these subtle but fatal errors.
AI-powered Resist Coating Uniformity & Defect Monitoring uses computer vision to continuously inspect resist film application in real-time. It tracks film consistency, detects defects like bubbles, streaks, and particles, and identifies non-uniform thickness across the wafer, before the wafer reaches the exposure tool.
Variations across wafer radius or from batch-to-batch can result in uneven exposure or resist development.
Bubbles, streaks, or particles are difficult to catch without consistent high-resolution inspection.
Poor edge bead removal (EBR) or aging resist causes contamination at the wafer edge, reducing usable area.
Defects are often discovered only after exposure or development, too late for correction.
High-speed cameras scan coated wafers for surface anomalies and coating consistency before exposure.
Visual data is processed to detect microns of variation across wafer zones and flag abnormal patterns.
Identifies EBR failures, air bubble entrapments, and inconsistent spin profiles that can compromise quality.
Automatically stops defective wafers from progressing and recommends rework, saving exposure tool cycles.


We begin by identifying the optimal inspection point, typically between the spin coater and EBR or before bake, to ensure full visual access before exposure.
Camera and lighting systems are tuned to work across multiple resist chemistries and coating speeds, with configurable profiles for each product line.
Our models are trained using historical coated wafer data, including edge bead patterns, film breaks, nozzle defects, and particulate contamination.
The inspection system sends “pass,” “hold,” or “re-coat” commands directly to your MES system, while simultaneously logging heatmaps and coating stats per wafer ID for traceability.
Stay updated with the trending and most impactful tech insights. Check out the expert analyses, real-world applications, and forward-thinking ideas that shape the future of AI Computer Vision and innovation.
Fuel is one of the largest recurring expenses in container yard operations, yet it is often treated as a fixed cost rather than a controllable variable. Most operators focus on throughput, turnaround time, and capacity utilization, but overlook how daily inefficiencies quietly inflate fuel consumption. The truth is straightforward: fuel costs are rarely high because […]

CEO & Co-founder
How to Speed Up Yard Operations In any container yard, time behaves like currency. Every minute a truck waits at the gate, every container that sits idle without clear movement, quietly adds to operational cost. Yet, many yards continue to function with fragmented visibility, delayed coordination, and reactive decision-making. The result is predictable: longer turnaround […]

CEO & Co-founder
6:10 AM. The first truck entered the yard. The driver is in a hurry. The queue behind him is already being built. At the gate, a few seconds decide everything. A number is read. A seal is “assumed” to be intact. A quick look confirms “no visible damage.” The barrier lifts. Everything seems normal. Until […]

CEO & Co-founder