{"id":45461,"date":"2024-04-22T11:30:52","date_gmt":"2024-04-22T18:30:52","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45461"},"modified":"2025-12-16T06:09:01","modified_gmt":"2025-12-16T14:09:01","slug":"blueprint-for-success","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/a-blueprint-for-semiconductor-manufacturing-success-with-ai\/ml-adoption","title":{"rendered":"A Blueprint for Semiconductor Manufacturing Success with AI\/ML Adoption"},"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-45461'><div class='entry-content-wrapper clearfix'>\n<section  class='av_textblock_section av-m8he998v-5275451689aeff882dec7b91039b683b '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><h1 class=\"elementor-heading-title elementor-size-xxl\" style=\"text-align: center;\">A Blueprint for Semiconductor Manufacturing Success with AI\/ML Adoption<\/h1>\n<\/div><\/section>\n<div  class='av-post-metadata-container av-m8j64jv5-15ea8059134a4364707ea42f809fb62e 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\" >22. April 2024<\/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\/tignis-news\/\" >Tignis News<\/a><\/span><\/span><\/span><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1bv7v3t-a95a3fe20eb3c9b5d76211f468174fcf\">\n#top .hr.hr-invisible.av-1bv7v3t-a95a3fe20eb3c9b5d76211f468174fcf{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-1bv7v3t-a95a3fe20eb3c9b5d76211f468174fcf hr-invisible  avia-builder-el-5  el_after_av_post_metadata  el_before_av_image '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g2cr45-3964a2a7214c78abde75454149a44e98\">\n.avia-image-container.av-m8g2cr45-3964a2a7214c78abde75454149a44e98 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8g2cr45-3964a2a7214c78abde75454149a44e98 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8g2cr45-3964a2a7214c78abde75454149a44e98 av-styling- avia-align-center  avia-builder-el-6  el_after_av_hr  el_before_av_hr '   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-45462 avia-img-lazy-loading-not-45462 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/A-Blueprint-for-Semiconductor-Manufacturing-Success-with-AI-ML-Adoption-1030x361.jpg\" alt='Graphic of a semiconductor ship with a frame of the chip hovering above the main chip components in a blue background' title='A Blueprint for Semiconductor Manufacturing Success with AI ML Adoption'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/A-Blueprint-for-Semiconductor-Manufacturing-Success-with-AI-ML-Adoption-1030x361.jpg 1030w, \/wp-content\/uploads\/2025\/03\/A-Blueprint-for-Semiconductor-Manufacturing-Success-with-AI-ML-Adoption-300x105.jpg 300w, \/wp-content\/uploads\/2025\/03\/A-Blueprint-for-Semiconductor-Manufacturing-Success-with-AI-ML-Adoption-768x269.jpg 768w, \/wp-content\/uploads\/2025\/03\/A-Blueprint-for-Semiconductor-Manufacturing-Success-with-AI-ML-Adoption-705x247.jpg 705w, \/wp-content\/uploads\/2025\/03\/A-Blueprint-for-Semiconductor-Manufacturing-Success-with-AI-ML-Adoption.jpg 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-utdyj0-04a5177d68da034ad2092d06415322e1\">\n#top .hr.hr-invisible.av-utdyj0-04a5177d68da034ad2092d06415322e1{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-utdyj0-04a5177d68da034ad2092d06415322e1 hr-invisible  avia-builder-el-7  el_after_av_image  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-tuqj09-3cc37ce1123cba055c8eb4140e4e2149 av_one_full  avia-builder-el-8  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><section  class='av_textblock_section av-m8iwv53l-e669bce76592417acaa936d512e2720a '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><blockquote>\n<p class=\"elementor-heading-title elementor-size-xxl\"><strong>In semiconductor manufacturing, the integration of AI and ML (machine learning) is not just a passing trend; it is a seminal shift that is already enhancing many facets of high-volume manufacturing (HVM). The integration of these technologies is driving enhanced operational efficiency, improved product quality, and reduced maintenance costs. Let\u2019s explore the specific areas where these technologies are making an impact today.<\/strong><\/p>\n<\/blockquote>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g2fmrk-8b9f652136afad41086947b64f0762b7\">\n#top .hr.hr-invisible.av-m8g2fmrk-8b9f652136afad41086947b64f0762b7{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8g2fmrk-8b9f652136afad41086947b64f0762b7 hr-invisible  avia-builder-el-10  el_after_av_textblock  el_before_av_textblock '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-m8he9lup-6f39a1c6b87fcd486c683f22aa53116c '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><div>\n<p><strong>Process Optimization:<\/strong>\u00a0Machine learning models excel at automating and optimizing semiconductor manufacturing processes. By analyzing process and equipment data, ML algorithms can identify subtle and complex issues that traditional methods cannot recognize. The rapid troubleshooting and root-cause-analysis capabilities of AI\/ML solutions enable process engineers to proactively address manufacturing issues, driving improved product throughput and lower cost of goods sold (COGS). \u00a0This is especially beneficial in periods of high semiconductor demand when greater equipment availability has a direct and positive impact on product revenue.<\/p>\n<p><strong>Intelligent Maintenance:<\/strong>\u00a0Equipment downtime is a necessary evil in semiconductor manufacturing. Tools need to be well-maintained in order for them to properly manufacture devices that inherently have tight manufacturing tolerances. Performing too much maintenance is expensive and reduces equipment availability. Insufficient maintenance (e.g. run to fail) can be equally expensive if it results in \u201clong term down\u201d situations in addition to potentially scrapping the wafers in process when a tool goes down. AI\/ML delivers intelligent maintenance, identifying equipment failures before they occur, enabling equipment engineers to leverage scheduled downtime to include additional maintenance tasks that can eliminate unplanned and costly maintenance activities.<\/p>\n<p><strong>Equipment Inventory Management:<\/strong>\u00a0Legacy semiconductor manufacturers have additional challenges because many of the tools used in their fabs can be decades old. Maintaining a proper supply of critical replacement parts is essential to maintain high-volume manufacturing, especially for parts with limited availability. Even for companies that choose \u201crun to fail\u201d manufacturing practices, AI\/ML can provide clear visibility into the need for future replacement parts, enabling equipment support teams to be ready to address any equipment down situation with minimal impact on production throughput.<\/p>\n<p><strong>Quality Control:<\/strong>\u00a0In any manufacturing environment, process drift has always been a challenge for process engineers. Even the best designed processes will experience drift over time resulting in process variability than can affect quality.\u00a0 AI\/ML models are being used today to implement \u201crun to run\u201d process control that can actively manage process drift. Not only can process variation be reduced by 50% or more, chamber availability can also be increased by 1-3% providing additional capacity from the same tool fleet.<\/p>\n<p><strong>Cost Reduction:<\/strong>\u00a0One of the most significant impacts of AI and ML in semiconductor manufacturing is overall cost reduction. Every one of the aforementioned areas has a direct impact on cost reduction: Engineering efficiency, smarter maintenance and inventory management, and improved product quality all contribute to lower COGS for semiconductor manufacturers.<\/p>\n<p><strong>Challenges and Future Directions:<\/strong>\u00a0Despite the clear advantages, the integration of AI\/ML into existing semiconductor manufacturing processes is not without challenges. High-quality data is the lifeblood of AI, and ensuring the timely availability of high-fidelity equipment and process data is essential to the success of any AI\/ML implementation. Process engineering and equipment automation teams also need to be open to change, because AI\/ML is much more than just \u201cdoing the same thing, but faster\u201d. AI\/ML is already changing the manufacturing paradigm that has existed for decades, and will continue to do so. Those that embrace change will be the winners in the next era of semiconductor manufacturing.<\/p>\n<\/div>\n<p>\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2013<\/p>\n<div>\n<p>The future of AI\/ML is bright. As these technologies continue to advance, they will continue to unlock untapped potential within the semiconductor industry. In fact, other than fab personnel, AI\/ML is the only appreciating asset in your fab. \u00a0We can expect to see further improvements in efficiency, reductions in production costs, and the development of even more sophisticated chips primarily due to the benefits of AI\/ML.<\/p>\n<p>For Tignis, these advancements are particularly pertinent. Tignis is well-positioned to help semiconductor manufacturers and equipment OEMs to achieve new levels of automation and process control, ensuring that these companies remain at the leading edge of innovation.<\/p>\n<p>AI\/ML is not just supporting the semiconductor industry, it is reshaping it. As an industry, we are on the cusp of a new era, with AI\/ML acting as the catalysts for innovation.<\/p>\n<p><strong>Sources:<\/strong><\/p>\n<p>McKinsey &amp; Company. (2024, March 13). Insights on Artificial Intelligence.\u00a0<a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\">https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights<\/a><\/p>\n<p>McKinsey &amp; Company. (2021, April 2). Scaling AI in the sector that enables it: Lessons for semiconductor-device makers.\u00a0<a href=\"https:\/\/www.mckinsey.com\/industries\/semiconductors\/our-insights\/scaling-ai-in-the-sector-that-enables-it-lessons-for-semiconductor-device-makers\">https:\/\/www.mckinsey.com\/industries\/semiconductors\/our-insights\/scaling-ai-in-the-sector-that-enables-it-lessons-for-semiconductor-device-makers<\/a><\/p>\n<p>Intel Newsroom. (2024, February 21). Intel Launches World\u2019s First Systems Foundry Designed for the AI Era. <a href=\"https:\/\/newsroom.intel.com\/artificial-intelligence\/intel-labs-introduces-ai-diffusion-model-generates-360-degree-images-from-text-prompts\">https:\/\/newsroom.intel.com\/intel-foundry\/foundry-news-roadmaps-updates<\/a><\/p>\n<p>Intel Newsroom. (2023, November 3). Intel Labs Introduces AI Diffusion Model, Generates 360-Degree Images. <a href=\"https:\/\/newsroom.intel.com\/artificial-intelligence\/intel-labs-introduces-ai-diffusion-model-generates-360-degree-images-from-text-prompts\">https:\/\/newsroom.intel.com\/artificial-intelligence\/intel-labs-introduces-ai-diffusion-model-generates-360-degree-images-from-text-prompts<\/a><\/p>\n<p>TechOvedas. (2023, August 7). How AI is Revolutionizing the Semiconductor Industry.\u00a0<a href=\"https:\/\/techovedas.com\/how-ai-is-revolutionizing-the-semiconductor-industry\/\">https:\/\/techovedas.com\/how-ai-is-revolutionizing-the-semiconductor-industry\/<\/a><\/p>\n<\/div>\n<\/div><\/section><\/p><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":67,"featured_media":45462,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[414],"tags":[],"class_list":["post-45461","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tignis-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Blueprint for Success - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"In semiconductor manufacturing, the integration of AI and ML is a seminal shift that is already enhancing many facets of high-volume manufacturing.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cohu.com\/tignis\/a-blueprint-for-semiconductor-manufacturing-success-with-ai\/ml-adoption\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Blueprint for Success - 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