OEM : Semiconductor
GF is one of the world’s leading semiconductor manufacturers and the only one with a truly global footprint. We are redefining innovation and semiconductor manufacturing by developing feature-rich process technology solutions that provide leadership performance in pervasive high growth markets. As a steadfast partner, with a unique mix of design, development and fabrication services, GF works collaboratively alongside our customers to bring a broad range of innovative products to market. With a global customer base, a talented and diverse workforce and an at-scale manufacturing footprint spanning three continents, GF is delivering a new era of more.
Using ML For Improved Fab Scheduling
The exact number of available tools for each step varies as tools are taken offline for maintenance or repairs. Some steps, like diffusion furnaces, consolidate multiple lots into large batches. Some sequences, like photoresist processing, must adhere to stringent time constraints. Lithography cells must match wafers with the appropriate reticles. Lot priorities change continuously. Even the time needed for an individual process step may change, as run-to-run control systems adjust recipe times for optimal results.
At the fab level, machine learning can support improved cycle time prediction and capacity planning. At the process cell or cluster tool level, it can inform WIP scheduling decisions. In between, it can facilitate better load balancing and order dispatching. As a first step, though, all of these applications need accurate models of the fab environment, which is a difficult problem.
The GlobalFoundries group demonstrated the effectiveness of neural network methods for time constraint tunnel dispatching. The relationship between input parameters and cycle time is complex and non-linear. As discussed above, machine learning methods are especially useful in situations like this, where statistical data is available but exact modeling is difficult.
🖥️🚙 General Motors signs deal with GlobalFoundries for exclusive U.S. semiconductor production
The chip manufacturer will establish dedicated production capacity exclusively for key auto suppliers of the Detroit automaker at its semiconductor facility in upstate New York, according to the companies. Caulfield said the exclusive production for GM is expected to take two to three years to really ramp up.
Fabs Drive Deeper Into Machine Learning
For the past couple decades, semiconductor manufacturers have relied on computer vision, which is one of the earliest applications of machine learning in semiconductor manufacturing. Referred to as Automated Optical Inspection (AOI), these systems use signal processing algorithms to identify macro and micro physical deformations.
Defect detection provides a feedback loop for fab processing steps. Wafer test results produce bin maps (good or bad die), which also can be analyzed as images. Their data granularity is significantly larger than the pixelated data from an optical inspection tool. Yet test results from wafer maps can match the splatters generated during lithography and scratches produced from handling that AOI systems can miss. Thus, wafer test maps give useful feedback to the fab.