{"id":2369,"date":"2015-11-21T20:03:05","date_gmt":"2015-11-22T01:03:05","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/applied-materials-when-machines-talk-to-each-other\/"},"modified":"2015-11-21T20:03:05","modified_gmt":"2015-11-22T01:03:05","slug":"applied-materials-when-machines-talk-to-each-other","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/applied-materials-when-machines-talk-to-each-other\/","title":{"rendered":"Applied Materials- When machines talk to each other"},"content":{"rendered":"

Applied Materials- when machines talk to each other<\/p>\n

Applied Materials (AMAT), the global leader in Semiconductor manufacturing equipment has started on a path for the smart fab, a manufacturing facility where the machines talk to each other and transmit data from tool to tool to improve quality and efficiency. One of the most interesting components of the Smart Fab is the Automatic Defect Classification (ADC) server.<\/p>\n

The ADC is a server that automatically classifies images of defects from Defect Review (DR) tools. As seen below, defects in Semiconductor manufacturing process can be a scratch on the surface, a particle or an uneven critical dimension. To understand how tiny these defects are it is worth mentioning that the latest generation DR tools are capable of detecting defects the size of 10 nano meters, which is almost on the atomic level (to be precise 10 nanometers are equivalent to 20 silicon atoms).<\/p>\n

\"defects\"<\/a><\/p>\n

So how do does the ADC work and uses data?<\/p>\n

The ADC server is connected to several DR tools that constantly send the server pictures of defects. The ADC server is essentially a rack of hundreds of image processing (IP) computers that analyze the images and compare them to a database of known defects. Since each customer has its own process with unique defects, Application Engineers use machine learning tools to teach the server how the customer\u2019s unique defects look. In production once an image is sent to the server for classification, the IP computers use advanced statistical algorithms to compare the image to the image database, to complete the classification.<\/p>\n

 <\/p>\n

Value Creation<\/p>\n

Before the invention of the ADC server, semiconductor manufacturers had to employ a team of engineers who would sit all day and classify images manually. Therefore having an ADC server directly contributes to cost reduction by eliminating manual classification. More importantly, the ADC server speeds up and improves data integrity of classification. This faster and improved classification contributes to a tighter statistical process control (SPC) in the fab which improves semiconductor yields, which is a significant value driver.<\/p>\n

\"SPC\"<\/p>\n

Value Capture<\/p>\n

The ADC server comes with a hefty price tag of several million dollars. however its price tag pales in comparison to the bigger sales it enables. Since the ADC works only with Applied Materials Defect Review tools, it encourages customers to award a higher share of DR tools to Applied Materials. Many customers tend to split their Defect Review Capex between AMAT and its competitor, typically 75\/25 or 50\/50. The ADC server has led to customers awarding 100% or 80% share to AMAT, which can mean 10-15 tools at a $3M per tool.<\/p>\n","protected":false},"excerpt":{"rendered":"

Applied Materials is using machine learning and advanced statistical algorithms to improve semiconductor yields<\/p>\n","protected":false},"author":31,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[891,366],"class_list":["post-2369","hck-submission","type-hck-submission","status-publish","hentry","category-enhanced-statistics","category-machine-learning"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-driven-value-creation-value-capture-and-operating-models\/","yoast_head":"\nApplied Materials- When machines talk to each other - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/applied-materials-when-machines-talk-to-each-other\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Applied Materials- When machines talk to each other - 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