{"id":6916,"date":"2026-07-07T16:52:45","date_gmt":"2026-07-07T07:52:45","guid":{"rendered":"https:\/\/matlantis.com\/ja\/?post_type=event_seminar&#038;p=6916"},"modified":"2026-07-07T19:45:38","modified_gmt":"2026-07-07T10:45:38","slug":"semiconductor-webinar-20260723","status":"publish","type":"event_seminar","link":"https:\/\/matlantis.com\/ja\/resources\/event-seminar\/semiconductor-webinar-20260723\/","title":{"rendered":"Simulating Semiconductor Unit Processes with a Universal Machine-Learning Potential"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/matlantis.com\/wp-content\/uploads\/2026\/07\/thumbnail-1024x576.jpg\" alt=\"\" class=\"wp-image-6917\"\/><\/figure>\n\n\n\n<style>\r\n  a.apply-button {\r\n    display: inline-block;\r\n    background-color: #fdaa80 !important;\r\n    color: #ffffff !important;\r\n    padding: 20px 40px;\r\n    text-decoration: none !important;\r\n    border-radius: 6px;\r\n    font-weight: bold;\r\n    font-size: 20px;\r\n    text-align: center;\r\n    transition: background-color 0.3s !important;\r\n    position: relative;\r\n  }\r\n\r\n  a.apply-button:hover {\r\n    background-color: #ea514a !important;\r\n    color: #ffffff !important;\r\n  }\r\n\r\n  a.apply-button::after {\r\n    content: none !important;\r\n  }\r\n<\/style>\r\n\r\n<div style=\"text-align: center; margin-block: 3em;\">\r\n  <a href=\"#webinar-form\" class=\"apply-button\">\r\n    Register Now\u2193\u3000\r\n  <\/a>\r\n<\/div>\n\n\n\n<p><strong>This webinar presents a practical look at how machine-learning atomistic simulation is reshaping semiconductor process development in the angstrom era. <\/strong><\/p>\n\n\n\n<p>Drawing on worked examples across ALD, dry etch, CMP, and interface engineering, it shows how PFP and the Matlantis platform bring DFT-level chemical insight to the system sizes and timescales that real process questions demand \u2014 fast enough to inform design decisions before wafers are run.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Who should attend<\/strong><\/h2>\n\n\n\n<p>\u30fb<strong>Computational Materials Scientists<\/strong> with an interest in the semiconductor<\/p>\n\n\n\n<p>\u30fb<strong>Semiconductor Researchers<\/strong> seeking to accelerate their R&amp;D<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Outline<\/strong><\/h2>\n\n\n\n<p>As device architectures push into the angstrom era, process outcomes \u2014 film nucleation, etch selectivity, interface quality, planarization \u2014 are governed by surface chemistry that is hard to observe directly and costly to optimize through wafer runs alone. Atomistic simulation can close that gap, but conventional DFT is too slow for the system sizes and timescales real process questions demand, while classical force fields miss the chemistry.<\/p>\n\n\n\n<p>This webinar demonstrates how PFP, a universal machine-learning interatomic potential covering 96 elements, delivers DFT-level chemical insight at 10\u2075\u201310\u2077\u00d7 the speed on the Matlantis cloud platform \u2014 with no system-specific model training required.&nbsp;<\/p>\n\n\n\n<p>Through worked examples spanning the process flow, we will cover:<\/p>\n\n\n\n<p>1. Reaction pathway analysis of Co ALD precursors on Si substrates, including quantitative evaluation of ligand substitution effects for rational precursor design<\/p>\n\n\n\n<p>2. High-throughput screening of area-selective ALD candidates by combining PFP adsorption energies with regression models and Bayesian optimization<\/p>\n\n\n\n<p>3. Large-scale (~70,000-atom) dry etching of SiO\u2082 by HF gas and force-dependent material removal in CMP-style polishing, enabled by LightPFP<\/p>\n\n\n\n<p>4. Interface engineering: melting behavior at AlN\/Cu bonding interfaces and TaN\/Cu interfacial thermal resistance validated against experiment<\/p>\n\n\n\n<p>5. Predicting properties beyond energies and forces using PFP Descriptors<\/p>\n\n\n\n<p>Whether you develop precursors, own a unit process, or are building in-house simulation capability, <strong>you&#8217;ll leave with a concrete picture of what atomistic modeling can answer today \u2014 and where it fits in your development cycle.<\/strong><\/p>\n\n\n\n<style>\r\n  a.apply-button {\r\n    display: inline-block;\r\n    background-color: #fdaa80 !important;\r\n    color: #ffffff !important;\r\n    padding: 20px 40px;\r\n    text-decoration: none !important;\r\n    border-radius: 6px;\r\n    font-weight: bold;\r\n    font-size: 20px;\r\n    text-align: center;\r\n    transition: background-color 0.3s !important;\r\n    position: relative;\r\n  }\r\n\r\n  a.apply-button:hover {\r\n    background-color: #ea514a !important;\r\n    color: #ffffff !important;\r\n  }\r\n\r\n  a.apply-button::after {\r\n    content: none !important;\r\n  }\r\n<\/style>\r\n\r\n<div style=\"text-align: center; margin-block: 3em;\">\r\n  <a href=\"#webinar-form\" class=\"apply-button\">\r\n    Register Now\u2193\u3000\r\n  <\/a>\r\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Speakers<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<p><strong><strong><strong>Qing-Jie Li<\/strong><\/strong><\/strong><\/p>\n\n\n\n<p>Senior Application Scientist, <br>Matlantis Inc.<\/p>\n\n\n\n<p>After receiving his Ph.D.in Materials Science and Engineering from Johns Hopkins University, he was a Postdoctoral Associate at MIT, where his research focused on developing and applying machine learning interatomic potentials (MLIP) for energy materials. He then worked as a Senior Engineer at Samsung Semiconductor US, contributing to computational materials discovery for battery applications before joining Matlantis, Inc. He is currently focused on sustainable materials solutions through the application of computational materials science and artificial intelligence (AI).<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1180\" height=\"1177\" src=\"https:\/\/matlantis.com\/wp-content\/uploads\/2025\/07\/qj.jpg\" alt=\"presenter\" class=\"wp-image-5456\"\/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<p><strong>Joshua Young<\/strong><\/p>\n\n\n\n<p>Senior Application Scientist, <br>Matlantis Inc.<\/p>\n\n\n\n<p>Joshua received his Ph.D. in Materials Science and Engineering from Drexel University, followed by appointments at the Naval Research Laboratory, Binghamton University, and the New Jersey Institute of Technology. Over the course of his career, he has used a wide variety of computational techniques, including density functional theory, molecular dynamics, and materials informatics\/machine learning, to study and design materials for electronics, batteries, catalysis, and more.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"473\" height=\"473\" src=\"https:\/\/matlantis.com\/wp-content\/uploads\/2025\/05\/Joshua_square.png\" alt=\"presenter\" class=\"wp-image-5079\"\/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Webinar Details<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Date and Time<\/strong><\/td><td>Thursday, July 23, 2026<br>8:00 AM PDT \/ 11:00 AM EDT \/ 4:00 PM BST \/ 5:00 PM CEST<br>(12:00 AM (midnight) JST, July 24)<\/td><\/tr><tr><td><strong>Location<\/strong><\/td><td>Online (Zoom)<\/td><\/tr><tr><td><strong>Fee<\/strong><\/td><td>Free<\/td><\/tr><tr><td><strong>Notes<\/strong><\/td><td>*This webinar is intended specifically for R&amp;D professionals and academic researchers. We kindly ask individuals from competing organizations to refrain from accessing this content.<br><br>*A Japanese-narrated on-demand version of the same content is available <a href=\"https:\/\/matlantis.com\/ja\/resources\/event-seminar\/ondemand-semiconductor\/\" target=\"_blank\" rel=\"noreferrer noopener\">here<\/a> for Japanese-speaking audiences.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"webinar-form\">Apply here<\/h2>\n\n\n\n<noscript>\n<iframe loading=\"lazy\" src=\"https:\/\/go.marketing.matlantis.com\/l\/1051562\/2026-07-06\/2l1hq\" width=\"100%\" height=\"500\" type=\"text\/html\" frameborder=\"0\" allowTransparency=\"true\" style=\"border: 0\"><\/iframe>\n\n<\/noscript>\n\n<script type=\"text\/javascript\">\n(function () {\n  \/\/ 1.ja\u7528\u30d5\u30a9\u30fc\u30e0\u306eURL\u306b\u5dee\u3057\u66ff\u3048\u308b\uff08\"\"\u306e\u4e2d\uff09\n  var jaUrlBase = \"https:\/\/go.marketing.matlantis.com\/l\/1051562\/2026-07-06\/2l1hq\";\n  \/\/ 2.en\u7528\u30d5\u30a9\u30fc\u30e0\u306eURL\u306b\u5dee\u3057\u66ff\u3048\u308b\uff08\"\"\u306e\u4e2d\uff09\n  var enUrlBase = \"https:\/\/go.marketing.matlantis.com\/l\/1051562\/2026-07-06\/2l1hq\";\u3000\n  \/\/ 3.\u30d5\u30a9\u30fc\u30e0\u306e\u9ad8\u3055\u3092\u5909\u66f4\u3059\u308b\uff08\u6570\u5b57\u90e8\u5206\uff09\n  var iframeHeight = 680;\n\n  function createOrUpdateIframe() {\n    var path = window.location.pathname;\n    var baseUrl = path.indexOf(\"\/en\/\") !== -1 ? enUrlBase :\n                  path.indexOf(\"\/ja\/\") !== -1 ? jaUrlBase : enUrlBase;\n\n    var params = window.location.search || \"\";\n    var src = baseUrl + (params ? 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