Applied Materials Introduces New Playbook for Process Control Based on Big Data and AI
March 16 2021 - 7:30AM
Applied Materials, Inc. today unveiled a major innovation in
process control that uses Big Data and AI technology to help
semiconductor manufacturers accelerate node development, speed time
to revenue and earn more profits over the life of a node.
Semiconductor technology is becoming increasingly complex and
expensive, and reducing the time needed to develop and ramp
advanced nodes can be worth billions of dollars to chipmakers
around the world. Success is gated by the ability to detect and
correct defects which is becoming increasingly difficult as line
widths shrink and turn nuisance particles into yield killers.
Likewise, 3D transistor formation and multiprocessing techniques
introduce subtle variations that can multiply to create
yield-killing defects that are vexing and time-consuming to
root-cause.
Applied Materials is solving these challenges with a new
playbook for process control designed to bring the benefits of Big
Data and AI technology to the core of chipmaking technology.
Applied’s solution consists of three elements that work together in
real time to find and classify defects faster, better and more cost
effectively than legacy approaches. The three elements are:
New Enlight® Optical
Wafer Inspection System: in development for five years,
the Enlight system combines industry-leading speed with high
resolution and advanced optics to collect more yield-critical data
per scan. The Enlight system architecture improves the economics of
optical inspection, resulting in a 3x reduction in the cost of
capturing critical defects as compared to competing approaches. By
dramatically improving cost, the Enlight system allows chipmakers
to insert many more inspection points in the process flow. The
resulting availability of Big Data enhances “line monitoring,”
statistical process control methods that can predict yield
excursions before they occur, immediately detect excursions so that
wafer processing can be halted to protect yields, and enable
root-cause traceback to accelerate corrective actions and the
return to high-volume manufacturing.
New ExtractAI™ Technology: developed by
Applied’s data scientists, ExtractAI technology solves the most
difficult problem of wafer inspection: the ability to quickly and
accurately distinguish yield-killing defects from the millions of
nuisance signals or “noise” generated by high-end optical scanners.
ExtractAI is the only solution in the industry that creates a
real-time connection between the Big Data generated by the optical
inspection system and the eBeam review system that classifies
specific yield signals so that by inference, the Enlight system
resolves all of the signals on the wafer map, differentiating yield
killers from noise. ExtractAI technology is incredibly efficient;
it characterizes all of the potential defects on the wafer map
after reviewing only 0.001% of the samples. The result is an
actionable map of classified defects that accelerates semiconductor
node development, ramp and yield. The AI technology is adaptive and
quickly identifies new defects during high-volume production while
progressively improving its performance and effectiveness as more
wafers are scanned.
SEMVision® eBeam
Review System: the SEMVision system is the most advanced
and widely used eBeam review technology in the world. With its
industry-leading resolution, the SEMVision system trains the
Enlight system with ExtractAI technology to classify yield-killing
defects and distinguish defects from noise. By working together in
real time, the Enlight system, ExtractAI technology and SEMVision
system help customers identify new defects as they are introduced
in the manufacturing flow, enabling higher yields and
profitability. The large installed base of SEMVision G7 systems is
already compatible with the new Enlight system and ExtractAI
technology.
“Being able to quickly and accurately distinguish yield-killing
defects from noise is something fab engineers have struggled with
for more than 30 years,” said Dan Hutcheson, chairman and CEO of
VLSIresearch. “Applied Materials’ Enlight system with ExtractAI
technology is a breakthrough approach that solves this challenge
and, because the AI gets smarter the more the system is used, it
helps chipmakers increase their revenue per wafer over time.”
“Applied’s new playbook for process control combines Big Data
and AI to deliver an intelligent and adaptive solution that
accelerates our customers’ time to maximum yield,” said Keith
Wells, group vice president and general manager, Imaging and
Process Control at Applied Materials. “By combining our
best-in-class optical inspection and eBeam review technologies, we
have created the industry’s only solution with the intelligence to
not only detect and classify yield-critical defects but also learn
and adapt to process changes in real time. This unique capability
enables chipmakers to ramp new process nodes faster and maintain
high capture rates of yield-critical defects over the lifetime of
the process.”
The new Enlight system with ExtractAI technology is the
fastest-ramping inspection system in Applied’s history and is
already being used in production at all leading-edge foundry-logic
customers worldwide. The SEMVision system has been the industry’s
leading eBeam review for over 20 years, with more than 1,500
systems at customer fabs around the world.
About Applied MaterialsApplied Materials,
Inc. (Nasdaq: AMAT) is the leader in materials engineering
solutions used to produce virtually every new chip and advanced
display in the world. Our expertise in modifying materials at
atomic levels and on an industrial scale enables customers to
transform possibilities into reality. At Applied Materials,
our innovations make possible the technology shaping the future.
Learn more at www.appliedmaterials.com.
Contact:Ricky Gradwohl (editorial/media)
408.235.4676Michael Sullivan (financial community) 408.986.7977
Photos accompanying this announcement are available
at:https://www.globenewswire.com/NewsRoom/AttachmentNg/e365db96-6a86-40c6-a435-2364d2eed4f1 andhttps://www.globenewswire.com/NewsRoom/AttachmentNg/a4e5de01-5069-42f3-9c25-b71a80cc0c8a
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