{"id":46466,"date":"2021-03-16T06:51:59","date_gmt":"2021-03-16T13:51:59","guid":{"rendered":"https:\/\/www.cohu.com\/?p=46466"},"modified":"2025-04-29T06:53:58","modified_gmt":"2025-04-29T13:53:58","slug":"into-to-ai-pc-the-next-level-of-process-control","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/intro-to-ai-pc-the-next-level-of-process-control","title":{"rendered":"Intro to AI-PC: The Next Level of Process Control"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141\">\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-parallax .av-parallax-inner{\nbackground-repeat:no-repeat;\nbackground-image:url(\/wp-content\/uploads\/2025\/03\/resources-banner-1500x333.jpg);\nbackground-position:50% 50%;\nbackground-attachment:scroll;\n}\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 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class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ig608p-4f8d1c0453663a84f522f5ebe321d648\">\n.flex_column.av-ig608p-4f8d1c0453663a84f522f5ebe321d648{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ig608p-4f8d1c0453663a84f522f5ebe321d648 av_one_full  avia-builder-el-1  avia-builder-el-no-sibling  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb\">\n#top .av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb{\npadding-bottom:10px;\ncolor:#ffffff;\nfont-size:3.5vw;\n}\nbody .av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n#top #wrap_all .av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-special-heading-tag{\nfont-size:3.5vw;\n}\n.av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .special-heading-inner-border{\nborder-color:#ffffff;\n}\n.av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-subheading{\nfont-size:25px;\n}\n\n@media only screen and (min-width: 768px) and (max-width: 989px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-special-heading-tag{\nfont-size:50px;\n}\n.av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-subheading{\nfont-size:25px;\n}\n}\n\n@media only screen and (min-width: 480px) and (max-width: 767px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-special-heading-tag{\nfont-size:47px;\n}\n.av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-subheading{\nfont-size:20px;\n}\n}\n\n@media only screen and (max-width: 479px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-special-heading-tag{\nfont-size:33px;\n}\n.av-special-heading.av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb .av-subheading{\nfont-size:15px;\n}\n}\n<\/style>\n<div  class='av-special-heading av-m8g29ww6-7c8f5d1e1b0dcf1d73c51bc2382622fb av-special-heading-h1 custom-color-heading blockquote modern-quote  avia-builder-el-2  avia-builder-el-no-sibling  av-inherit-size av-linked-heading'><h1 class='av-special-heading-tag '  itemprop=\"headline\"  >PHYSICS, MACHINES AND DATA<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div><\/div><div id='after_section_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-46466'><div class='entry-content-wrapper clearfix'>\n<section  class='av_textblock_section av-m8j77h33-1fc7d4d75db3cf7fa778a7854351a78c '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><h1 style=\"text-align: center;\">Intro to AI-PC: The Next Level of Process Control<\/h1>\n<\/div><\/section>\n<div  class='av-post-metadata-container av-m8j5u2ic-153fe0332261bac4c61a77a678ca1977 av-metadata-container-align-center  avia-builder-el-4  el_after_av_textblock  el_before_av_hr  av-metadata-container-1'><div class='av-post-metadata-container-inner'><span class=\"av-post-metadata-content av-post-metadata-meta-content\"><span class=\"av-post-metadata-content av-post-metadata-published\"><span class=\"av-post-metadata-published-date\" >16. March 2021<\/span><\/span><span class=\"av-post-metadata-content av-post-metadata-separator\">\/<\/span><span class=\"av-post-metadata-content av-post-metadata-category\"><span class=\"av-metadata-before av-metadata-before-categories\">in<\/span><span class=\"av-post-metadata-category-link\" ><a href=\"https:\/\/www.cohu.com\/category\/thought-leadership\/\" >Thought Leadership<\/a><\/span><\/span><\/span><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8j5v5vc-d7a68dccff0196cf338cb50c25aa15b2\">\n#top .hr.hr-invisible.av-m8j5v5vc-d7a68dccff0196cf338cb50c25aa15b2{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8j5v5vc-d7a68dccff0196cf338cb50c25aa15b2 hr-invisible  avia-builder-el-5  el_after_av_post_metadata  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-84nirh-2573d3f0d18d6ff1bad09211234f04f8 av_one_full  avia-builder-el-6  el_after_av_hr  el_before_av_hr  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g2v7tl-aa4222f9a339cd8f671408acb999bfc3\">\n.avia-image-container.av-m8g2v7tl-aa4222f9a339cd8f671408acb999bfc3 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8g2v7tl-aa4222f9a339cd8f671408acb999bfc3 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8g2v7tl-aa4222f9a339cd8f671408acb999bfc3 av-styling- avia-align-center  avia-builder-el-7  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-46468 avia-img-lazy-loading-not-46468 avia_image ' src=\"\/wp-content\/uploads\/2025\/04\/Intro-to-AI-PC_-The-Next-Level-of-Process-Control-Banner-1024x358-1.png\" alt='' title='Intro-to-AI-PC_-The-Next-Level-of-Process-Control-Banner-1024x358'  height=\"358\" width=\"1024\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/04\/Intro-to-AI-PC_-The-Next-Level-of-Process-Control-Banner-1024x358-1.png 1024w, \/wp-content\/uploads\/2025\/04\/Intro-to-AI-PC_-The-Next-Level-of-Process-Control-Banner-1024x358-1-300x105.png 300w, \/wp-content\/uploads\/2025\/04\/Intro-to-AI-PC_-The-Next-Level-of-Process-Control-Banner-1024x358-1-768x269.png 768w, \/wp-content\/uploads\/2025\/04\/Intro-to-AI-PC_-The-Next-Level-of-Process-Control-Banner-1024x358-1-705x246.png 705w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1qfqr25-f282c28b3a2027a2e85c4f1bea29dcb0\">\n#top .hr.hr-invisible.av-1qfqr25-f282c28b3a2027a2e85c4f1bea29dcb0{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-1qfqr25-f282c28b3a2027a2e85c4f1bea29dcb0 hr-invisible  avia-builder-el-8  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div><\/p>\n<div  class='flex_column av-19v49r1-4c29ced287ceab51165e24d028af3cf3 av_one_full  avia-builder-el-9  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g2wtqy-9d8da27f64d89571a2f88bc7428e3eea\">\n#top .av_textblock_section.av-m8g2wtqy-9d8da27f64d89571a2f88bc7428e3eea .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8g2wtqy-9d8da27f64d89571a2f88bc7428e3eea '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Manufacturing process control is all about getting as close as possible to set points to optimize quality, throughput, and yield. But traditional process control models such as PID loops, advanced process control (APC), and multivariable process control (MPC) are limited in their ability to achieve these goals. A new model that incorporates artificial intelligence (AI), overcomes the issues and is delivering dramatically improved outcomes.<\/p>\n<p>Originally, process control was dependent on an operator\u2019s gut feel or specialized expertise. Two similar models, APC and later MPC, emerged to help automate the control function. With APC, a static controller tunes a process for a single variable, such as the quality of gas coming out of a distillation process. MPC addresses process interactions by tuning multiple process variables with a static controller.<\/p>\n<p>However, PID loops, APC, and MPC are static models, meaning unless or until someone manually intervenes, the same logic is applied continuously. In addition, most controllers are reactive in that they try something, look at the results, and then use that feedback to adjust. MPC is promising but it has not really caught on in industry because historic methods of engineering make its simulations inordinately expensive.<\/p>\n<p><strong>Feedback, feedforward, and predictive control<\/strong><\/p>\n<p>AI-PC is the next step in the process control maturity curve. It optimizes or replaces historic PID loops and APC loops by putting AI on the controllers and using machine learning models to generate and refine control processes. It trains off existing APC models, simulations, or historical process sensor data and is taught to apply sensor feedback from quality checks at the end of the process, allowing the logic of the control algorithm to get smarter. Besides intelligent feedback control, AI-PC\u2019s extremely high computational speeds support feedforward and predictive control. These unique characteristics are transformative:<\/p>\n<ul>\n<li><u>Drastically higher speeds enable real-time process control<\/u>: Compared to the advisory approach of static process control, AI-PC controllers can run predictive physical simulations millions of times faster, allowing for real-time process control. Instead of asking one question every 15 minutes to make decisions based on the current process state, AI-PC can ask 1,000 questions a second, enabling it to simulate and predict thousands of futures as part of the control loop.<u>\u00a0<\/u><\/li>\n<li><u>Machine learning drives continuous process improvement<\/u>: By learning to recognize when a process is performing poorly and retraining the process controller in a feedback loop, process quality increases over time. AI-PC homes in on a machine\u2019s specific process and its unique environmental and operating conditions to continuously sharpen the process.<\/li>\n<\/ul>\n<p><strong>Optimized business outcomes<\/strong><\/p>\n<p>For discrete and process manufacturers alike, the advantages of real-time, continuously improving process control are many. For instance:<\/p>\n<ul>\n<li><u>Quality<\/u>: Tightening variances without exceeding spec improves process and product quality.<\/li>\n<li><u>Yields<\/u>: Limiting variations in quality avoids product scrap and increases throughput yields.<\/li>\n<li><u>Uptime<\/u>: Improving process quality minimizes unplanned outages from plant or equipment tripping.<\/li>\n<li><u>Sustainability<\/u>: Controlling emissions, energy efficiency, and other environmental parameters enables a more sustainable operation.<\/li>\n<li><u>Safety<\/u>: Operating within process constraints improves the safety of people and processes.<\/li>\n<li><u>New business models<\/u>: Pricing strategies can be aligned to the quality of products produced, such as premium pricing for higher-end products.<\/li>\n<\/ul>\n<p><strong>Manufacturing use cases<\/strong><\/p>\n<p>As semiconductor processes shrink into the single digit nanometers, sources of process variance are becoming increasingly harder to control. Most existing fabs are utilizing APC methods, which are not sophisticated enough to correct for significant sources of process variation. Ideally, for each process step, we would like to predict which set points or recipe will result in the highest possible yield. A feedforward predictive control uses metrology from the previous step to do this prediction. However, some processes \u2013 particularly those to enable next generation chips \u2013 are incredibly complicated, and their outcomes cannot be predicted without sophisticated simulation. It can take 10s of minutes or hours to run a single simulation step therefore they cannot be run on every single semiconductor wafer, and they cannot be run in real time.<\/p>\n<p>One of the leading semiconductor production equipment manufacturers chose AI-PC from Tignis to help control one of its next-generation semiconductor fabrication tools. They had a large number of process parameters that needed to be controlled with each wafer and to identify the right parameters required a high-fidelity simulation that was computationally expensive and slow. This limited how many times they could update the process parameters. With AI-PC, they are able to simulate thousands of possible \u00a0futures each second and apply the best process parameters in real time to each wafer produced.<\/p>\n<p>Crude oil distillation is another area where AI-PC is improving process control. Even though the multi-physics models used to simulate oil distillation are completely different from the models used in semiconductor manufacturing, the technique of training an AI-PC model is still effective. An AI-PC model can predict the results of the critical ASTM D86 lab measurement, recommend the set points that are predicted to yield the optimum measurement, and then continuously compare the actual results to the predicted ones. \u00a0At any point where there is drift between what AI-PC predicts will happen and what actually happens, the AI-PC model retrains to create an updated model which accounts for the process drift.<\/p>\n<p>Regardless of the industry or the physical process being modeled, continuously improving feedback, real-time feedforward and predictive control make AI-PC the future of process control.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":67,"featured_media":46468,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-46466","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thought-leadership"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - 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