{"id":120449,"date":"2025-07-10T09:59:24","date_gmt":"2025-07-10T01:59:24","guid":{"rendered":"https:\/\/honwaygroup.com\/quantum-machine-learning-applied-to-semiconductor-manufacturing-for-the-first-time-australian-csiro-research-achieves-global-first-breakthrough\/"},"modified":"2025-07-11T10:54:13","modified_gmt":"2025-07-11T02:54:13","slug":"quantum-machine-learning-applied-to-semiconductor-manufacturing-for-the-first-time-australian-csiro-research-achieves-global-first-breakthrough","status":"publish","type":"post","link":"https:\/\/honwaygroup.com\/en\/quantum-machine-learning-applied-to-semiconductor-manufacturing-for-the-first-time-australian-csiro-research-achieves-global-first-breakthrough\/","title":{"rendered":"Quantum Machine Learning Applied to Semiconductor Manufacturing for the First Time: Australian CSIRO Research Achieves Global First Breakthrough"},"content":{"rendered":"\n<p class=\"has-medium-font-size wp-block-paragraph\">Currently, semiconductor manufacturing faces increasingly complex technical challenges, especially in critical processes such as ohmic contact modeling, where traditional artificial intelligence methods are gradually reaching their limits. However, a new study from Australia&#8217;s national science agency CSIRO has for the first time applied Quantum Machine Learning (QML) to the analysis of real semiconductor manufacturing process data, demonstrating superior performance over classical methods. This breakthrough not only proves the potential of quantum technology in small-sample, high-dimensional environments but also opens up new possibilities for chip design and process optimization. This article will take you deep into this world-first research achievement and how it might rewrite the future development path of semiconductors. <\/p>\n\n<div class=\"wp-block-rank-math-toc-block\" id=\"rank-math-toc\"><h2>Table of Contents<\/h2><nav><ul><li class=\"\"><a href=\"#%E5%8D%8A%E5%B0%8E%E9%AB%94%E5%BB%BA%E6%A8%A1%E9%81%87%E7%93%B6%E9%A0%B8%EF%BC%8C%E9%87%8F%E5%AD%90%E9%81%8B%E7%AE%97%E5%8A%A0%E5%85%A5%E6%88%B0%E5%B1%80\">Semiconductor Modeling Faces Bottlenecks, Quantum Computing Enters the Fray<\/a><\/li><li class=\"\"><a href=\"#qkar%EF%BC%9A%E5%B0%88%E7%82%BA%E5%B0%8F%E6%A8%A3%E6%9C%AC%E8%A8%AD%E8%A8%88%E7%9A%84%E9%87%8F%E5%AD%90%E5%9B%9E%E6%AD%B8%E6%9E%B6%E6%A7%8B\">QKAR: A Quantum Regression Architecture Designed for Small Samples<\/a><\/li><li class=\"\"><a href=\"#%E8%A7%A3%E6%B1%BA%E8%B3%87%E6%96%99%E7%A8%80%E7%BC%BA%E8%88%87%E9%9D%9E%E7%B7%9A%E6%80%A7%E6%8C%91%E6%88%B0-%E9%87%8F%E5%AD%90%E5%84%AA%E5%8B%A2%E9%A1%AF%E7%8F%BE\">Addressing Data Scarcity and Non-Linear Challenges: Quantum Advantage Emerges<\/a><\/li><li class=\"\"><a href=\"#%E7%B5%90%E5%90%88%E9%87%8F%E5%AD%90%E8%88%87%E7%B6%93%E5%85%B8%E6%8A%80%E8%A1%93-%E5%AF%A6%E9%A9%97%E9%A9%97%E8%AD%89%E9%82%81%E5%87%BA%E9%97%9C%E9%8D%B5%E4%B8%80%E6%AD%A5\">Combining Quantum and Classical Technologies: A Crucial Step in Experimental Validation<\/a><\/li><li class=\"\"><a href=\"#%E5%8D%8A%E5%B0%8E%E9%AB%94%E5%BB%BA%E6%A8%A1%E8%BF%8E%E4%BE%86%E6%96%B0%E5%85%B8%E7%AF%84%EF%BC%9F%E9%87%8F%E5%AD%90%E6%8A%80%E8%A1%93%E6%BD%9B%E5%8A%9B%E7%84%A1%E9%99%90\">Semiconductor Modeling Enters a New Paradigm? Quantum Technology&#8217;s Potential is Limitless<\/a><\/li><li class=\"\"><a href=\"#%E7%B5%90%E8%AA%9E%EF%BC%9A%E5%BE%9E%E5%AF%A6%E9%A9%97%E5%AE%A4%E5%88%B0%E6%99%B6%E5%9C%93%E5%BB%A0%E7%9A%84%E9%87%8F%E5%AD%90%E9%9D%A9%E6%96%B0\">Conclusion: Quantum Innovation from Laboratory to Fab<\/a><\/li><\/ul><\/nav><\/div>\n\n<h2 class=\"wp-block-heading has-ast-global-color-0-color has-text-color has-link-color wp-elements-c941d796101ad7c0e1a9ee93962ce19c\" id=\"&#x534A;&#x5C0E;&#x9AD4;&#x5EFA;&#x6A21;&#x9047;&#x74F6;&#x9838;&#xFF0C;&#x91CF;&#x5B50;&#x904B;&#x7B97;&#x52A0;&#x5165;&#x6230;&#x5C40;\">Semiconductor Modeling Faces Bottlenecks, Quantum Computing Enters the Fray<\/h2>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">In the highly precise and increasingly complex processes of the semiconductor industry, how to effectively model becomes a major challenge. Especially for critical steps like Ohmic Contact formation, which involve multiple process parameters and non-linear relationships, traditional artificial intelligence and machine learning methods are gradually showing their limitations.<\/p>\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/honwaygroup.com\/wp-content\/uploads\/2025\/07\/2025-07-01_100620.avif\"><img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"410\" src=\"https:\/\/honwaygroup.com\/wp-content\/uploads\/2025\/07\/2025-07-01_100620.avif\" alt=\"Image: Schematic diagram of the GaN HEMT Ohmic contact formation modeling process based on quantum machine learning. Source: Advanced Science\" class=\"wp-image-120420\" srcset=\"https:\/\/honwaygroup.com\/wp-content\/uploads\/2025\/07\/2025-07-01_100620.avif 800w, https:\/\/honwaygroup.com\/wp-content\/uploads\/2025\/07\/2025-07-01_100620-300x154.webp 300w, https:\/\/honwaygroup.com\/wp-content\/uploads\/2025\/07\/2025-07-01_100620-768x394.webp 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/a><figcaption class=\"wp-element-caption\">Image: Schematic diagram of the GaN HEMT Ohmic contact formation modeling process based on quantum machine learning. Source: Advanced Science<\/figcaption><\/figure>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Recently, a research team from Australia&#8217;s national science agency CSIRO, in collaboration with international partners from China and Hong Kong, for the first time validated a new model centered on Quantum Machine Learning (QML), specifically designed to solve modeling challenges in semiconductor manufacturing, and successfully applied it to experimental data analysis. This research achievement is hailed as a world first and has been published in the journal Advanced Science.<\/p>\n\n<h2 class=\"wp-block-heading has-ast-global-color-0-color has-text-color has-link-color wp-elements-043bb03bb0c7973f9de1dd15e96411a3\" id=\"qkar&#xFF1A;&#x5C08;&#x70BA;&#x5C0F;&#x6A23;&#x672C;&#x8A2D;&#x8A08;&#x7684;&#x91CF;&#x5B50;&#x56DE;&#x6B78;&#x67B6;&#x69CB;\">QKAR: A Quantum Regression Architecture Designed for Small Samples<\/h2>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">This research focused on manufacturing samples of GaN HEMT (Gallium Nitride High Electron Mobility Transistor). The research team developed an innovative model called the &#8220;<strong>Quantum Kernel Alignment Regressor (QKAR)<\/strong>&#8221; using only 159 experimental data points. This model integrates Pauli-Z feature mapping with a trainable quantum kernel layer, enabling it to extract deep patterns from extremely small datasets while maintaining high precision.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">QKAR&#8217;s performance surpassed that of seven traditional CML (Classical Machine Learning) regressors, demonstrating significant advantages across various regression error metrics (such as MAE, MSE, RMSE). Particularly in terms of Mean Absolute Error (MAE), QKAR achieved 0.338 \u03a9\u00b7mm, showcasing excellent accuracy and generalization capabilities.<\/p>\n\n<h2 class=\"wp-block-heading has-ast-global-color-0-color has-text-color has-link-color wp-elements-0b2f7eb9fdd7b5128fe3b6ae07b0532d\" id=\"&#x89E3;&#x6C7A;&#x8CC7;&#x6599;&#x7A00;&#x7F3A;&#x8207;&#x975E;&#x7DDA;&#x6027;&#x6311;&#x6230;-&#x91CF;&#x5B50;&#x512A;&#x52E2;&#x986F;&#x73FE;\">Addressing Data Scarcity and Non-Linear Challenges: Quantum Advantage Emerges<\/h2>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">In the past, although classical machine learning models were widely used in manufacturing modeling, they often faced problems such as overfitting and insufficient generalization when dealing with small samples, high-dimensional parameters, and complex non-linear scenarios. Quantum machine learning, on the other hand, can leverage its unique kernel properties to capture highly interactive relationships between complex parameters and exhibit stronger fault tolerance and data adaptability.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">As co-author Zeheng Wang stated: &#8220;The semiconductor industry is increasingly challenged by both data scarcity and process complexity. Our research shows that well-designed quantum models can identify underlying patterns that classical models cannot grasp.&#8221;<\/p>\n\n<h2 class=\"wp-block-heading has-ast-global-color-0-color has-text-color has-link-color wp-elements-daab7d90f236e0c5af17ca2031fc041a\" id=\"&#x7D50;&#x5408;&#x91CF;&#x5B50;&#x8207;&#x7D93;&#x5178;&#x6280;&#x8853;-&#x5BE6;&#x9A57;&#x9A57;&#x8B49;&#x9081;&#x51FA;&#x95DC;&#x9375;&#x4E00;&#x6B65;\">Combining Quantum and Classical Technologies: A Crucial Step in Experimental Validation<\/h2>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">A major highlight of this research is that although the adopted model architecture runs on a simulator, it is compatible with current NISQ (Noisy Intermediate-Scale Quantum) hardware, suggesting potential for direct deployment in the future. The team further manufactured new GaN devices, providing physical validation to support the model&#8217;s predictions, marking an important step towards the practical application of quantum machine learning models.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">In addition, the research also established a unified PCA (Principal Component Analysis) preprocessing pipeline, ensuring a fair comparison between models and demonstrating the rigor and scientific nature of the research design.<\/p>\n\n<h2 class=\"wp-block-heading has-ast-global-color-0-color has-text-color has-link-color wp-elements-82c1ea04af662c60ecb55394187f49c3\" id=\"&#x534A;&#x5C0E;&#x9AD4;&#x5EFA;&#x6A21;&#x8FCE;&#x4F86;&#x65B0;&#x5178;&#x7BC4;&#xFF1F;&#x91CF;&#x5B50;&#x6280;&#x8853;&#x6F5B;&#x529B;&#x7121;&#x9650;\">Semiconductor Modeling Enters a New Paradigm? Quantum Technology&#8217;s Potential is Limitless<\/h2>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">With the development of quantum computing technology, its applications are gradually expanding from chemical simulation, cryptography, and material research to manufacturing engineering, with semiconductor processes becoming another frontier application area. The successful validation of the QKAR model represents quantum machine learning&#8217;s potential to become a new paradigm for future chip design and manufacturing modeling, especially in scenarios with scarce data and complex process conditions.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Researchers point out that although CML methods still have room for optimization, QML has already demonstrated its potential as an auxiliary or alternative tool. In the future, as quantum processor scale and fidelity continue to improve, these quantum models will have greater opportunities to make significant contributions in actual industrial processes.<\/p>\n\n<h2 class=\"wp-block-heading has-ast-global-color-0-color has-text-color has-link-color wp-elements-8cbb15cda5feb78807e86418bc92eeec\" id=\"&#x7D50;&#x8A9E;&#xFF1A;&#x5F9E;&#x5BE6;&#x9A57;&#x5BA4;&#x5230;&#x6676;&#x5713;&#x5EE0;&#x7684;&#x91CF;&#x5B50;&#x9769;&#x65B0;\"><strong>Conclusion: Quantum Innovation from Laboratory to Fab<\/strong><\/h2>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Semiconductors are the cornerstone of modern technology, and their manufacturing processes are a focal point of technological challenges and innovation. Research units such as Australia&#8217;s CSIRO are injecting new momentum into semiconductor manufacturing through quantum technology. The application of QML may not only improve manufacturing efficiency and reduce costs but is also expected to bring about a revolution in the entire industry, from data modeling to process optimization.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\">This transformation, driven by quantum machine learning, might just be the critical turning point in the chip race over the next decade.<\/p>\n\n<p class=\"wp-block-paragraph\">Reference:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Reshaping Future Chips, Quantum Machine Learning Shines Brightly<\/li>\n\n\n\n<li>Quantum Machine Learning Shines in Semiconductor Chip Design<\/li>\n\n\n\n<li>Quantum machine learning improves semiconductor manufacturing for first time<\/li>\n<\/ul>\n\n<p class=\"wp-block-paragraph\">(Source of the first picture: pexels)<\/p>\n\n<p class=\"wp-block-paragraph\">We offer customized adjustments to the grinding process, tailored to meet processing requirements for maximum efficiency.<\/p>\n\n<p class=\"wp-block-paragraph\" style=\"line-height:0.8\">Feel free to contact us and we will have specialist available to answer your questions.<\/p>\n\n<p class=\"wp-block-paragraph\" style=\"line-height:0.8\">If you need customized quotations, you\u2019re also welcome to contact us.<\/p>\n\n<p class=\"wp-block-paragraph\" style=\"line-height:0.8\">Customer Service Hours: Monday to Friday 09:00~18:00 (GMT+8)<\/p>\n\n<p class=\"wp-block-paragraph\" style=\"line-height:0.8\">Phone: +886<a href=\"https:\/\/www.google.com\/search?q=%E5%AE%8F%E5%B4%B4&amp;oq=%E5%AE%8F%E5%B4%B4&amp;gs_lcrp=EgZjaHJvbWUqBggAEEUYOzIGCAAQRRg7MhAIARAuGK8BGMcBGIAEGI4FMgYIAhBFGDsyBwgDEAAYgAQyBggEEEUYPTIGCAUQRRg9MgYIBhBFGD0yBggHEEUYQdIBCDE5MDhqMGo3qAIIsAIB&amp;sourceid=chrome&amp;ie=UTF-8\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">7 223 1058<\/a><\/p>\n\n<p class=\"wp-block-paragraph\" style=\"line-height:0.8\">If you have a subject that you want to know or a phone call that is not clear, you are welcome to send a private message to Facebook~~<\/p>\n\n<p class=\"wp-block-paragraph\" style=\"line-height:0.8\">Honway Facebook: <a href=\"https:\/\/lihi.cc\/LhR8c\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/www.facebook.com\/honwaygroup<\/a><\/p>\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-ast-global-color-0-background-color has-background wp-element-button\" href=\"https:\/\/honwaygroup.com\/en\/honway-raw-materials\/\" target=\"_blank\" rel=\"noreferrer noopener\">We are Honway, by controlling our raw materials from the source, we ensure the quality of our products and offer you customized options.<\/a><\/div>\n<\/div>\n\n<ul class=\"wp-block-jetpack-sharing-buttons has-normal-icon-size jetpack-sharing-buttons__services-list\" id=\"jetpack-sharing-serivces-list\">\n<\/ul>\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<p class=\"wp-block-paragraph\">You may be interested in&#8230;<\/p>\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\"><p>[wpb-random-posts]<\/p>\n<\/div>\n<\/div>\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Currently, semiconductor manufacturing faces increasingly complex technical challenges, especially in critical processes such as ohmic contact modeling, where traditional artificial intelligence methods are gradually reaching their limits. However, a new study from Australia&#8217;s national science agency CSIRO has for the first time applied Quantum Machine Learning (QML) to the analysis of real semiconductor manufacturing process data, demonstrating superior performance over classical methods. This breakthrough not only proves the potential of quantum technology in small-sample, high-dimensional environments but also opens up new possibilities for chip design and process optimization. This article will take you deep into this world-first research achievement and how it might rewrite the future development path of semiconductors.<\/p>\n","protected":false},"author":1,"featured_media":120432,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[791,3166],"tags":[8898],"class_list":["post-120449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-knowledge-column","category-technological-insights","tag-semiconductor"],"_links":{"self":[{"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/posts\/120449","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/comments?post=120449"}],"version-history":[{"count":0,"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/posts\/120449\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/media\/120432"}],"wp:attachment":[{"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/media?parent=120449"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/categories?post=120449"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/honwaygroup.com\/en\/wp-json\/wp\/v2\/tags?post=120449"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}