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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.

Assembly Line

Using ML For Improved Fab Scheduling

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✍️ Author: Katherine Derbyshire

πŸ”– Topics: Production Planning, Machine Learning

🏭 Vertical: Semiconductor

🏒 Organizations: GlobalFoundries

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.

Read more at Semiconductor Engineering

Lockheed Martin And GlobalFoundries Collaborate To Advance Innovation And Resiliency Of Chips For National Security

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πŸ”– Topics: Partnership

🏒 Organizations: Lockheed Martin, GlobalFoundries

Lockheed Martin (NYSE LMT) and GlobalFoundries (Nasdaq GFS) (GF) announced a strategic collaboration to advance U.S. semiconductor manufacturing and innovation and to increase the security, reliability and resiliency of domestic supply chains for national security systems. This collaboration will enable Lockheed Martin to more quickly and affordably produce secure solutions that increase the competitiveness and national security of the United States.

The companies will leverage GF’s differentiated technology and trusted manufacturing practices to increase anti-fragility in microelectronics systems and supply chains. The collaboration will explore critical needs in semiconductor innovation and secure manufacturing across a range of advanced and next-generation chip technologies, including 3D heterogeneous integration for optimized chip packaging that improves performance; silicon photonics for low-power and high-speed data transport; and gallium nitride on silicon to help chips work at higher temperatures. The companies will also work to develop a chiplet ecosystem to produce chips more rapidly and affordably.

The collaboration between Lockheed Martin and GF directly supports the CHIPS and Science Act’s objectives of increasing traceability, provenance, and onshore production of critical semiconductor technologies to strengthen national and economic security and domestic supply chains.

Read more at Lockheed Martin News

πŸ–₯οΈπŸš™ General Motors signs deal with GlobalFoundries for exclusive U.S. semiconductor production

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✍️ Author: Michael Wayland

πŸ”– Topics: Partnership

🏒 Organizations: General Motors, GlobalFoundries

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.

Read more at CNBC

Fabs Drive Deeper Into Machine Learning

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✍️ Author: Anne Meixner

πŸ”– Topics: machine learning, machine vision, defect detection, convolutional neural network

🏭 Vertical: Semiconductor

🏒 Organizations: GlobalFoundries, KLA, SkyWater Technology, Onto Innovation, CyberOptics, Hitachi, Synopsys

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.

Read more at Semiconductor Engineering