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Add to Calendar2026-02-11 00:00:002026-02-13 00:00:00SEMICON Korea 2026TRANSFORM TOMORROWThe future of the semiconductor industry starts here.Join us at SEMICON Korea 2026 to shape tomorrow together. HoursFebruary 11, 2026 | 10:00-17:00 (Last entry 16:30)February 12, 2026 | 10:00-17:00 (Last entry 16:30)February 13, 2026 | 10:00-16:00 (Last entry 15:30)VenueCOEX (Hall A, B, C, D, E, Grand Ballroom, Platz and ASEM Ballroom)Westin Seoul ParnasGrand InterContinental Seoul ParnasScale550 Exhibitors, 2409 booths (2025: 501 exhibitors, 2301 booths)Seoul South KoreaSEMI.org[email protected]America/Los_Angelespublic
Setting the Stage: Industry Drivers & SCC Initiative Context
• Semiconductor Industry Decarbonization Challenges
• The Need for Quantified Business Cases
• SCC Initiative Background & Development Journey
• Vision for Industry Adoption & Collaboration
• Tool Architecture & Methodology
• Key Inputs & Assumptions
• Understanding the Outputs & Metrics
• Live Demonstration & Example Scenario
• Current Limitations & Future Development Opportunities
9:36 am
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9:50 am
Jeff Rudnik
Director of Environmental Sustainability & Net Zero
ASM
Industry Use Cases & Practical Applications
• Evaluating Decarbonization Projects
• Comparing Alternative Mitigation Strategies
• Supporting Internal Investment Decisions
• Lessons Learned from Early Applications
• Opportunities for Industry Collaboration
The semiconductor industry is under increasing pressure to decarbonize its operations, particularly with regard to Scope 1 emissions — direct greenhouse gas emissions (GHG) from owned or controlled sources. Yet many companies face a persistent challenge: how to make a clear, consistent, and financially credible case for emissions reduction investments. To help address this need, the Semiconductor Climate Consortium (SCC) has developed a Cost-Benefit Calculator — a simplified and flexible tool that offers a structured starting point for evaluating carbon emissions reduction projects. The calculator enables users to estimate the Net Present Cost (NPC) per ton of CO₂ equivalent emissions reduction, helping to translate environmental impact into business-relevant terms.
Join the SEMI's Semiconductor Climate Consortium (SCC) Scope 1 Working Group and document authors for a webinar discussing their journey creating the Cost-Benefit Calculator and outlining the use cases for this tool. The Cost-Benefit Calculator is a functional spreadsheet with built in report creation tools. For business managers who need to calculate emissions for Scrope 1.
Building the business case for emission reductions
9:00 am - 10:00 am
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Add to Calendar2026-06-16 09:00:002026-06-16 10:00:00SCC: Cost Benefit Calculator WebinarThe semiconductor industry is under increasing pressure to decarbonize its operations, particularly with regard to Scope 1 emissions — direct greenhouse gas emissions (GHG) from owned or controlled sources. Yet many companies face a persistent challenge: how to make a clear, consistent, and financially credible case for emissions reduction investments. To help address this need, the Semiconductor Climate Consortium (SCC) has developed a Cost-Benefit Calculator — a simplified and flexible tool that offers a structured starting point for evaluating carbon emissions reduction projects. The calculator enables users to estimate the Net Present Cost (NPC) per ton of CO₂ equivalent emissions reduction, helping to translate environmental impact into business-relevant terms.Join the SEMI's Semiconductor Climate Consortium (SCC) Scope 1 Working Group and document authors for a webinar discussing their journey creating the Cost-Benefit Calculator and outlining the use cases for this tool. The Cost-Benefit Calculator is a functional spreadsheet with built in report creation tools. For business managers who need to calculate emissions for Scrope 1.SCC members can download the Cost Benefit Calculator Report here.SEMI United StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
Register Today!
This workshop is designed to help participants understand:
The real-world deployment, impact, and lessons learned from AI techniques
Strategies for building observable and scalable multi-agent workflows
The industry transition from restrictive data silos toward autonomous discovery.
Methods for achieving tangible business results and automation through diverse AI applications
We’ve curated a technical agenda featuring Topics such as:
Agentic AI
Gaussian Process Regression
Time Series Modeling
Bayesian Optimization
And more...
You will walk away with actionable insights on:
Yield Enhancement through Edge-Driven Defect Detection and Classification
Demonstrates how edge AI leverages real-time sensor and image data—through virtual metrology, anomaly detection, and SPC-integrated feedback loops—to enable early defect detection, classification, and yield optimization.
From Prediction to Action: Causal AI for Real-Time Root-Cause Analysis in Semiconductor Manufacturing
How causal AI unifies multi-modal fab data into an intelligence layer to move beyond predictive alerts toward rapid root cause identification, prescriptive actions, and continuous improvement—significantly reducing RCA time and improving yield and operational efficiency.
From Data Silos to Autonomous Discovery: Agentic AI in Semiconductors
Agentic AI in semiconductor workflows as autonomous, decision-making systems (beyond RAG) that orchestrate multi-agent reasoning across complex fab data—requiring robust data foundations, domain-grounded algorithms, SME-driven knowledge (including reasoning traces), and structured evaluation frameworks (agentic harness) to ensure reliable, scalable deployment.
Making Sense of Equipment Time‑Series Data: From Signals to Insight
Introduces how semiconductor equipment time-series data (sensor signals, traces, run-to-run data) can be processed and modeled to uncover equipment behavior over time, enabling engineers to detect drift, changes, and anomalies for improved monitoring and troubleshooting.
Achieving Business Results from Air-gapped Agentic AI Automation in Semiconductor Manufacturing - Lessons from the Field
Highlights real-world deployment of agentic AI in fabs, demonstrating how autonomous systems are driving yield, capacity, and operational gains across engineering workflows (FDC, yield, test, maintenance) while navigating constraints like data privacy and legacy integration—along with practical adoption strategies and hands-on use cases.
And more...
SEMI HQ 673 S Milpitas Blvd. Milpitas, CA95035 United States
Morning, Day 1: December 4, 2024
8:00 am
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9:00 am
Registration & Breakfast
9:00 am
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12:30 pm
Session #1 - Data Needs for Digital Twins - Data Sources, Integration & Interoperability
This session will explore the critical data requirements for creating and maintaining digital twins. Integrating multi-modal data sources (sensors, enterprise systems, equipment, etc.) that feed into digital twins, ensuring that they accurately reflect their physical counterparts is a minimum necessity. This session will also cover data integration techniques (from various factory systems), and interoperability to support dynamic digital twins.
9:00 am
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9:10 am
Mark da Silva
Senior Director, Smart Manufacturing
SEMI
Welcome Remarks
9:10 am
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9:35 am
Michael Neel
Director of Marketing
Inficon
Keynote - Enabling Autonomous Operations Through Integrated Digital Twins
9:35 am
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10:00 am
Robert Baseman
Senior Technical Staff Member
IBM
Semantic Models for Microelectronic Manufacturing & Development
10:00 am
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10:25 am
David McKee
Technology Leader & Managing Partner
Counterpoint Tech/DTC
Journey to Twins: A toolkit for Capabilities & Data to Digital Twins
10:25 am
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10:40 am
Coffee Break - Sponsored by INFICON
10:40 am
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11:05 am
Rad Desiraju
Director, WW Industry Advisory
Microsoft
Overcoming Data Interoperability Challenges for Effective Digital Twins
11:05 am
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11:30 am
Wei Zhao
Head of Process Integration
Athinia
Chris Han-Adebekun
VP, Business Development
Athinia
Building Process & Supply Chain Digital Twins through Integrated Data Assets and Secure Data Sharing
11:30 am
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12:30 pm
Dave Henshall
Vice President of Business Development and Government Relations
Semiconductor Research Corporation
Chris Ritter
CTO
Idaho National Lab
Special Session - SmartUSA – Semiconductor Digital Twin Manufacturing USA Institute
12:30 pm
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2:00 pm
Lunch
Afternoon, Day 1: December 4, 2024
2:00 pm
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5:00 pm
Session #2 - Data Standards for Digital Twins: Managing Unclean Factory Data
This session will focus on the importance of data standards for digital twins in semiconductor manufacturing, particularly in the context of dealing with unclean factory data. It will explore the challenges associated with unclean data and discuss strategies for cleaning and standardizing to ensure seamless ingestion into digital twins at various levels. Best practices for data validation, transformation, and integration as well as how to implement robust data standards to enhance reliability and accuracy of digital twins.
Sponsored by - Seeq
2:00 pm
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2:25 pm
Dr. Joseph Ervin
Product Line Head of Semiverse Solutions Group
Lam Research
Keynote - Addressing Future Challenges in the Stochastics Era of Patterning with EUV Dry Resist
New patterning technologies will be needed to advance the device roadmap to 2 nm and beyond. Minimum pitch scaling will be exceptionally difficult to achieve at these advanced nodes, even with EUV patterning. Stochastic effects using wet resists during EUV patterning become problematic at the nanoscale, and can lead to line collapse, bridging, unacceptable line edge roughness (LER) and line width roughness (LWR). These stochastic effects present challenges when optimizing the tradeoffs between EUV resist resolution, dose, sensitivity, line edge / line width roughness, defectivity and cost.
In this talk, we will review the basics of dry resist technology, including dry resist equipment requirements, process flows and process integration modeling. In addition, we will present data that documents the advantages of dry resist technology over wet resist technology, demonstrating superior image quality, scaling and yield. We will conclude the talk by demonstrating a digital twin of the process integration and usage of optimization to achieve improved LER/LWR characteristics.
2:25 pm
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2:50 pm
Alan Weber
Vice President, New Product Innovations
Cimetrix by PDF Solutions
Charting the Digital Twin Standardization Path: An Iterative Implementation and Abstraction Process
2:50 pm
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3:15 pm
Brett Brimhall
Deloitte
3:15 pm
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3:30 pm
Coffee Break - Sponsored by INFICON
3:30 pm
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3:55 pm
Andres Torres
Engineer and Key Expert
Siemens
Feature Engineering as the bridge to Semantic Factory Data
3:55 pm
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4:20 pm
Michael Bowcutt
Director of Sales Engineering for Romaric products at camLine
camLine USA
Control Software Integration with Digital Twins: How Virtual Data Aligns with Physical Reality
4:20 pm
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5:20 pm
Moderator
Kenneth Smith
VP Growth Markets
Seeq
Sean Tropsa
Principal Analytics Engineer
Seeq
Panel: Harmonizing Standards for Semiconductor Digital Twins
5:30 pm
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7:00 pm
Networking Reception - Sponsored by Sentient Cloud
Morning, Day 2: December 5, 2024
8:00 am
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9:00 am
Registration & Breakfast
9:00 am
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12:30 pm
Session #3 - Modeling High Complexity Semiconductor Processes
This session will explore the integration of digital twin technology in modeling high complexity semiconductor processes. As the semiconductor industry advances, the need for precise and efficient modeling techniques becomes crucial. Digital Twins offer a transformative approach to simulate and optimizes these processes, though challenges exist. Cases studies and future innovations will also be covered in this session.
9:00 am
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9:05 am
Mark da Silva
Senior Director, Smart Manufacturing
SEMI
Welcome Remarks
9:05 am
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9:30 am
Umesh Kelkar
Master/Vice President, Semiconductor Products Group
Applied Materials
Keynote: Key Enablers for Semiconductor Equipment and Process Digital Twins: Accelerated Computing (AC), Artificial Intelligence (AI) and Physics Modeling
9:30 am
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9:55 am
Prith Bannerjee
Chief Technology Officer
Ansys
AI Driven Digital Twins for Semiconductor Manufacturing
9:55 am
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10:20 am
Xi-Wei Lin
Executive Director, Applications Engineering
Synopsys
AI and Physics-based Models for Technological Development with High Complexity Semiconductor Processes
10:20 am
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10:45 am
Coffee Break - Sponsored by PhysicsX
10:45 am
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11:10 am
Amit Lal
Professor, Cornell University
Cornell
Peter Doerschuk
Professor
Cornell University
Ben Davaji
Assistant Professor
Northeastern University
Digital Twin Flow Using AI/ML Applied to Ferroelectric Memory
11:10 am
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11:35 am
John Maculley
Semiconductor Industry Strategy Consultant
Dassault Systemes
Leveraging Virtual Twins and AI for Enhanced Fabrication Technology Co-Optimization in Semiconductor Manufacturing
11:35 am
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12:30 pm
Pushkar Apte
Strategic Technology Advisor and Global Data-AI Lead
SEMI
Panel Discussion
12:30 pm
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2:00 pm
Lunch - Sponsored by Dassault Systemes
Afternoon, Day 2: December 5, 2024
2:00 pm
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5:00 pm
Session #4 - GenAI & AI/ML Role in the Development of Digital Twins - Implementation Strategies for DT
Integrating AI/GenAI & Digital Twins - Explore the cutting-edge integration of Generative AI (GenAI) and Artificial Intelligence/Machine Learning (AI/ML) in the creation and evolution of digital twins. This session will delve into how GenAI & AI/ML technologies are revolutionizing the simulation, prediction, and real-time replication of physical assets in semiconductors through case studies and practical applications.
2:00 pm
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2:25 pm
Kamaljeet Ghotra
Senior Director, Go-To Market and Digital Strategy
PDF Solutions
Keynote - Accelerating Digital Twins with Gen AI
The rise of sophisticated AI tools, including generative AI, is revolutionizing the semiconductor industry by enabling the analysis of vast datasets to generate valuable insights. Executives in the sector see generative AI as a transformative force rather than just another tool. They believe it can deliver significant value, particularly in manufacturing, operations, and maintenance, its potential for process and equipment analysis, Predictive maintenance and smart diagnostics.
As fabs adopt smart manufacturing and virtual modeling it is pertinent for digital twins to leverage technologies like generative AI for data generation, Data Synthesis/Augmentation, Real Time Analysis, Model Optimization (AI enhanced physics models) , Rapid Prototyping and Accelerated Design and Development with data-driven model-based learning.
2:25 pm
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2:50 pm
Peter Lendermann
CTO
D-SIMLAB
About Cross-Fertilization between Digital Twins and AI Techniques for Capacity Planning and WIP Flow Optimisation in Semiconductor Fabs
2:50 pm
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3:15 pm
Kiran Karunakaran
Chief Technology Officer
Via Automation
Developing Explainable Digital Twins for the Semiconductor Industry Using GenAI, Knowledge Graphs, and ML Techniques
3:15 pm
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3:30 pm
Coffee Break - Sponsored by PhysicsX
3:30 pm
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3:55 pm
Mark Huntington
Managing Director of North America
PhysicsX
Real-Time Digital Twins for Semiconductor Manufacturing: Accelerating Multi-Physics Optimization with Physics-Based AI
3:55 pm
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4:20 pm
Becky Kelderman
Solutions Sales Manager
Rockwell Automation
Advancing Industrial Data Architectures & Models for AI in Process Digital Twins
4:20 pm
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5:20 pm
Moderator
Mithun Kamat
Partner
McKinsey
Panel - Rewiring organizations for AI / Digital twin: a business-backed view of successful implementation and challenges in adoption of Digital twins in the semiconductor industry
The SEMI Smart Manufacturing Initiative is hosting a two-day workshop titled "AI Techniques in Semiconductor Manufacturing" at SEMI HQ in Milpitas, CA, on August 5–6, 2026. This event is part of a continuing series focused on integrating advanced AI into the semiconductor landscape. The workshop is designed to help participants understand: The real-world deployment, impact, and lessons learned from AI techniques. Strategies for building observable and scalable multi-agent workflows. The industry transition from restrictive data silos toward autonomous discovery. Methods for achieving tangible business results and automation through diverse AI applications.
AI Techniques in Semiconductor Manufacturing
[IN-PERSON EVENT]
Aug 5-6, 2026
8:00 am - 5:00 pm
Off
Add to Calendar2026-08-05 08:00:002026-08-06 17:00:00AI Techniques in Semiconductor Manufacturing The SEMI Smart Manufacturing Initiative is hosting a two-day workshop titled "AI Techniques in Semiconductor Manufacturing" at SEMI HQ in Milpitas, CA, on August 5–6, 2026. This event is part of a continuing series focused on integrating advanced AI into the semiconductor landscape. The workshop is designed to help participants understand: The real-world deployment, impact, and lessons learned from AI techniques. Strategies for building observable and scalable multi-agent workflows. The industry transition from restrictive data silos toward autonomous discovery. Methods for achieving tangible business results and automation through diverse AI applications.SEMI HQ 673 S Milpitas Blvd. Milpitas, CA 95035 United StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
Register Now
Smart Sensors and Edge Intelligence for Advanced Process Control
Overview: Explore the latest sensor technologies—optical, thermal, vibration, and chemical—and how embedded AI at the edge enables real-time control loops for critical wafer fab processes. Includes case studies on inline metrology and adaptive control.
Key Topics: Sensor fusion and calibration, Edge inference for process drift detection, Integration with APC and FDC system
8:00 am
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8:45 am
Registration & Breakfast
8:45 am
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8:55 am
Anshu Bahadur
SEMI
Day 1 - Welcome All
Welcome & SEMI Manufacturing Coalitions Overview
8:55 am
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9:30 am
Russell Dover
General Manager, Service Product Line
LAM
Keynote: Vision – Edge Inference – Process Control, APC, FDC
9:30 am
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9:55 am
James Bramante
Senior Data Scientist
INFICON
Managing Edge AI for Smart Sensor Process Control
9:55 am
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10:20 am
Robert Hillinger
Industry Leader Semiconductor
Kistler
Advanced Process Control with Piezoelectric Sensor Fusion for Semiconductor Manufacturing
10:20 am
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10:35 am
Coffee Break
10:35 am
-
11:00 am
Chris Dickens
Application Eng
Beckhoff Automation
A Unified Industrial Platform for Production-Ready AI
11:00 am
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11:25 am
Gokul Sathya
Research Scientist
Siemens
Physical AI on the Manufacturing Floor: From Virtual Commissioning to Deployment
11:25 am
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11:50 am
Aumkar Renavikar
Chief Product Officer, Co-Founder & CTO
Parallax Worlds
Intelligent Robots & Digital Twins for Advanced Manufacturing
11:50 am
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12:05 pm
Chakravarthy Elumalai
CTO
Ray Vector
Sensor as the Eyes and Ears of AI
12:05 pm
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1:00 pm
Networking Lunch
Afternoon, Day 1: March 18th, 2026
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Yield Enhancement through Edge-Driven Defect Detection and Classification
Overview: Present how edge AI models process sensor and image data to identify yield-impacting defects early in the process. Discuss virtual metrology, anomaly detection, and feedback loops for yield optimization.
Key Topics: Real-time defect classification, Sensor-based yield prediction, Integration with SPC and yield dashboards
1:00 pm
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1:05 pm
Karim Somani
SEMI
SEMI Fab Owners Alliance (FOA)
1:05 pm
-
1:35 pm
Surya Kalidindi
Co-Founder
Multiscale
Physics-Aware AI for Semiconductor Manufacturing Process Optimization: Deployed Systems and the Edge AI Frontier
1:35 pm
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2:00 pm
Maryia Kurdina
AI R&D Group Leader, Technology & AI Design Research Center
TEL
Real-Time, Tool-Independent Health Monitoring Using Gaussian Process Confidence Bands on Sensor Signals
2:00 pm
-
2:25 pm
Avni Agarwal
Co-Founders & CTO
SixSense
Applications of AI-Based Defect Classification for Yield Improvement: From FEOL (Front end-of line) in Fabs to Assembly and Test
2:25 pm
-
2:40 pm
Coffee Break
2:40 pm
-
3:05 pm
Arjun Hegde
Senior Application Engineering Manager
KLA
Edge-Driven Yield Learning: Real-Time Deep Learning and HAR Defect Discovery in Advanced E-Beam Architectures
3:05 pm
-
3:30 pm
Prasad Bachiraju
Senior Director, Business Development
Onto Innovation
Reducing Rework and Boosting Fab Capacity with Edge AI and Metrology-Driven Predictive Analytics
3:30 pm
-
3:55 pm
Danny Krastev
Full Stack AI Developer
Microtronic
Overcoming the challenges of AI defect detection and classification of semiconductor macro defects
3:55 pm
-
4:20 pm
Chengli He
Industry Manager
MathWorks
From Data to Decisions: Edge AI for Yield, Defect Detection, and Predictive Insights
4:20 pm
-
4:35 pm
Stephen March
Semiconductor Leader
Schneider
Software Defined Industrial Automation: A Blueprint for AI-Ready, Real-Time Edge Operations in Semiconductor Manufacturing
4:35 pm
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6:35 pm
Reception
Morning, Day 2: March 19th, 2026
Autonomous WIP Movement: Robots, Sensors, and Edge AI Coordination
Overview: Dive into next-gen clean rooms – how intelligent robotics & AI‑enabled inspection elevate yield, reliability & efficiency.
Key Topics: Sensor-guided AGVs and AMRs, AI-based path optimization, Interoperability with fab logistics systems
8:00 am
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8:45 am
Registration & Breakfast
8:45 am
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8:55 am
Paul Carey and Melissa Grupen-Shemansky
SEMI
Day 2 - Welcome All
Day 1 Overview & MSIG Overview
8:55 am
-
9:30 am
Carlos O’Farrill
Global Key Account Manager
KUKA
Keynote: Smarter Fabs in Motion: How Edge AI and Sensor Networks Drive Autonomous Material Handling
9:30 am
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9:55 am
Vidya Vijay
Director, Business Development, Growth & Strategy
Nordson
Precision, Automation, and Beyond: The Role of Advanced Sensors in the Evolution of Semiconductor Manufacturing for effective Tool set-up, Predictive and Preventative Maintenance
9:55 am
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10:20 am
Joseph Tsao
US Sales Director
Solomon 3-D
Perception-Enabled Assurance of Semiconductor Manufacturing Systems
10:20 am
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10:35 am
Coffee Break
10:35 am
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11:00 am
Murali Krishna
Vice President of Products
Minds.ai
Using Edge AI models and Deep RL to improve yield, tool availability, and fab performance
11:00 am
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11:25 am
Michael Bowcutt
Director of Sales Engineering
CamLine
Solving the Interoperability Gap: Orchestrating and Emulating Mixed-Fleet Robotics for High-Yield Fabs
11:25 am
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11:50 am
Greg Berger
Solution Consultant
Rockwell Automation
Smart Modular Conveyance, the first step towards Autonomous Operations in Analog & Legacy fabs
11:50 am
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12:15 pm
Brian Zmikly
Sr. Director, US Enterprise & Government Sales, 5G Acceleration
Verizon
The 5G Fabric: Leveraging Next Gen Connectivity to Enable Automation and Intelligence
12:15 pm
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12:30 pm
Anshu Bahadur
SEMI
Robots in the Cleanroom: Future of Automation in Semiconductor Manufacturing
12:30 pm
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1:30 pm
Networking Lunch
Afternoon, Day 2: March 19th, 2026
Predictive Maintenance at the Edge: From Vibration to Vision
Overview: Highlight how edge-deployed AI models use sensor data (vibration, acoustic, thermal, visual) to predict equipment failures before they occur. Showcase deployment strategies and ROI from reduced downtime.
Key Topics: Edge analytics for tool health, Federated learning across equipment fleets, Maintenance scheduling optimization
1:30 pm
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2:05 pm
James Bramante
Senior Data Scientist
INFICON
Keynote: Predictive Maintenance at the Edge - AI Enabling PdM
2:05 pm
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2:30 pm
Michael Passow
AI Semiconductor Factory Automation Program Manager
IBM
Edge AI–Enabled Predictive Maintenance: Closing the Loop Between Equipment Health and Fab Operations
2:30 pm
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2:55 pm
Mauro Riboni
Technical lead – Alliance & Partnership
Bosch
The Edge Revolution: Predictive Maintenance Reborn with Edge AI
2:55 pm
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3:20 pm
Paul Johnson
Senior Manager – Digital Transformation
Edwards
The Challenge Of AI At The Edge – Making Use Of Advanced Sensors
3:20 pm
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3:35 pm
Coffee Break
3:35 pm
-
4:00 pm
Sainyam Galhotra
Co-founder & Faculty, Cornell University
Third AI Automation
Edge-based Defect Detection and Predictive Maintenance in Semiconductor Manufacturing
4:00 pm
-
4:25 pm
Sundeep Ahluwalia
Chief Product Officer
TDK SensEI
Edge AI: The Future of Industrial Machine Health Monitoring
4:25 pm
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4:50 pm
Kelli Case
Business Development Director, Senseye Predictive Maintenance
Siemens
How AI‑based Maintenance Can Reshape Semiconductor Operations By Cutting Downtime By Up To 50%
Unlocking Smarter, More Autonomous Fabs with Edge AI
As semiconductor fabs strive for higher yield, uptime, and efficiency, AI at the edge is becoming central to semiconductor manufacturing. Modern edge devices combine advanced sensors with embedded AI to monitor equipment, optimize energy, and detect anomalies in real time - without relying on the cloud.
This workshop will explore how AI-driven sensors and edge intelligence, integrated with IoT, 5G, and AI accelerators, enable scalable, resilient, and intelligent solutions tailored for semiconductor manufacturing. Learn how these technologies support:
Predictive maintenance to prevent downtime
Adaptive process automation for maximum efficiency
Real-time decision-making at the source of data generation
Join us to see how the convergence of sensors, edge AI, and connectivity is unlocking the next era of smart manufacturing.
Smarter Sensors, Smarter Fabs: AI at the Edge in Semiconductor Manufacturing
In-Person Workshop | March 18-19, 2026 | Milpitas, CA
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Add to Calendar2026-03-18 00:00:002026-03-19 00:00:00Smarter Sensors, Smarter Fabs: AI at the Edge in Semiconductor ManufacturingUnlocking Smarter, More Autonomous Fabs with Edge AIAs semiconductor fabs strive for higher yield, uptime, and efficiency, AI at the edge is becoming central to semiconductor manufacturing. Modern edge devices combine advanced sensors with embedded AI to monitor equipment, optimize energy, and detect anomalies in real time - without relying on the cloud.This workshop will explore how AI-driven sensors and edge intelligence, integrated with IoT, 5G, and AI accelerators, enable scalable, resilient, and intelligent solutions tailored for semiconductor manufacturing. Learn how these technologies support:Predictive maintenance to prevent downtimeAdaptive process automation for maximum efficiencyReal-time decision-making at the source of data generationJoin us to see how the convergence of sensors, edge AI, and connectivity is unlocking the next era of smart manufacturing.Milpitas, CA United StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
On Demand
Smart MFG
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Add to Calendar2025-05-15 00:00:002025-05-15 00:00:002025 SEMI China Smart Manufacturing Committee MeetingSuzhou Shi Jiangsu Sheng ChinaSEMI.org[email protected]America/Los_Angelespublic
