Wafer-Level Package handling and inspection/metrology platforms

Thomas Brillouet
R&D Manager
Recif Technologies

Abstract
The recent developments within the European Collaborative project TSV-Handy regarding the handling of different sizes and types of substrates, combined with integrated inspection and metrology, address several requirement aspects for Smart Manufacturing.The two main challenges of 3D ICs manufacturing, TSV and Fan-Out Wafer Level Packaging techniques, is the multiplication of heterogeneous types of substrates which are presenting different mechanical behaviours and physical properties, and the lack of efficient and clean handling solution in the back-end market. A modular equipment, which is able to manipulate as many types of wafers as possible, without any hardware reconfiguration and managed by a smart adaptive software, will help the end-users in gaining equipment flexibility, up-time and yield.This presentation will give an outlook on how the modular approach responds to these challenges.

Biography
Working for RECIF Technologies since 2000, Thomas BRILLOUET has evolved along with the company. Graduated in electrical and automatic engineering, he then started as a Field Service Engineer for RECIF USA, prior to becoming Technical Coordinator of the East Coast area. Thomas left the USA in 2005, and then successfully managed several Field Task Forces in different Asian countries.This strong field experience drove him to Development & Project Management. Named R&D Manager in RECIF HQ in 2012 he took in charge products developments since then and committed in multiple European Collaborative Projects, through different frameworks. He contributed in those programs as work-package or task leader from the very first EEMI450 through each 450mm development and demonstration program (NGC450 among them), and today TSV-HANDY.

Smart Manufacturing setups to achieve an AI based Digital Transmission within the EMS-industry

Markus M. Hoffmann
Head of Business Development Asia-Pacific
Zollner Elektronik AG

Abstract
1) DIGITAL TRANSMISSION within the EMS INDUSTRYWhat are the everyday challenges of an EMS company?2) INDUSTRY 4.0 & MADE IN CHINA 2025How geopolitical frame-conditions influence technical progress3) METHODS & TOOLSValue Stream MappingDigital FactoryMTMDFx4) SUPPORT TECHNOLOGY & AUTOMATIZATIONDigital TwinAutomized components handlingAugmented RealityCo-Robotics Solution5) AI & DEEP LEARNING WITHIN EMSThe 4 V´s of big dataAI support for qualityDeep Learning and Big Data for Predictive Maintenance6) INDUSTRY 5.0 supported by AIOptimize RFP-processesUsing 5G to increase automatizationNew business modell arise from Big Data7) Executive SummaryWhat does AI mean for the EMS-sectorWhat transition is needed to keep up the pace

Biography
Markus M. Hoffmann has more than 15 years of experience within the EMS-market, focusing on global direct customer care management. Developed and established processes as overall head for involved operational units in Eastern Europe, Asia & North America, Markus has built key account relationships with new and existing customers mainly within the Semiconductor (front- and back end) business. His latest role in Zollner is to force deep engagement in the definition and pursuit process for the EMS market in the Asia Pacific Region. Markus pursues business in the dedicated geographical area and market in accordance with the assigned Business Unit’s strategy same as identify new potential Business for key-sectors like Railway, Aerospace, Healthcare & Semidconductor. Beside his activities at Zollner, Markus acts as an International Observer Columnist & Podcast Panelist for Chinese International Radio (CRI). Markus was also part in this years SEMICON SEA (Advanced Packaging Forum) & SEMICON Taiwan (SiP Global Summit) as a Speaker and Panelist, sharing his idead on Smart Manufacturing & Hetergenous Integration.

Smart Manufacturing for Packaging Industry

James Song

Asml

Abstract
Smart manufacturing has become the focus for global manufacturing for productivity, quality, yield and performance improvement. With the digital connectivity and IIoT advancement, all these have become feasible. However, this remains a challenge for Backend packaging industry on process optimization and real time on-line control based on modeling and data analytics. One of the road blocks is that packaging equipment are not designed and ready for such evolution. In this presentation, we will share our ASMPT’s approaches on tackling these challenges and some of the ecosystems that we established to gear towards smart manufacturing.

Biography
He has been serving the semiconductor industry for 25 years and owns numerous patents relating to bonding apparatus. He has worked across multiple disciplines such as vision technology, wire bonding, third optical inspection, metrological equipment, package mold encapsulation, 3D sensing calibration and automation line for automotive camera assembly. His current focus is to establish an ecosystem to realize industry 4.0 and introducing AIoT to their customers

Automate#LikeABosch – BOSCH’s path to entire Fab Automation

Heinz Martin

Fabmatic

Abstract
Existing 200mm semiconductor fabs can master the challenges of a 24x7 production under highest cost and quality pressure by implementing intralogistics automation solutions. In the previous years many companies operating mature fabs understood that older does not mean obsolete and started or continued their journey towards higher automated production. Fabmatics developed a variety of automation solutions in the fields of material identification and tracking, storage, transport and handling. Over the past 10 Years the implementation of AMHS Systems and Components were implemented in the BOSCH 8” Fab in Reutlingen in “Step-by-Step” Approach while the Fab was continuously in Production and increased efficiency. Bosch obsesses this path to entire Fab Automation consequently and with great confidence, the final results show real value. In his presentation Heinz Martin Esser will explain the latest trends of customer requirements for automation of mature Semiconductor Fabs and how Fabmatics is tackling these challenges by implementing a comprehensive intralogistics automation system, which will provide customers a lot of benefits and advantages in operating such an automated fab.

Biography
Heinz Martin Esser, born in 1955, graduated from University in 1981 as a mechanical engineer. In 1985 he got an additional university degree in business administration.Strong Experiences in detailed engineering and project management for large and complex investments for nuclear industry and beverage and food industry. Later assigned as branch manager to build production clean rooms and facilities in semiconductor and pharmaceutical industry.Since 1999 he is Managing Director of Fabmatics GmbH respectively the precursor organizations of Roth & Rau – Ortner GmbH, Dresden and Ortner c.l.s. GmbH. The company provides advanced Fab Automation solutions, RFID products and applications like FOUP-N2 Purge systems and complex system expertise in AHMS installation and qualified services for the international semiconductor Industry.Since 2001 he is member of the board of directors of Silicon Saxony e.V., Dresden with now more than 350 member companies. In 2009 he was assigned to be the speaker of the board.Since 2008 he is also a member of the “SEMI” EAB (European Advisory Board).

Practical Machine Learning for Tools

Doug Suerich
Product Evangelist
PEER Group

Abstract
Semiconductor equipment makers (OEMs) are under pressure to produce tools that offer the maximum possible substrate throughput while maintaining high process quality levels. Although actual processing time is often constrained by physical limits based on the underlying requirements of the process itself, overall throughput in a tool is also subject to non-productive time delays such as transfer time, equipment speed limitations, and vacuum pump down. Therefore, minimizing non-productive tool time is a key element in maximizing overall throughput.This case study investigates efforts made to use machine learning to improve throughput in a complex cluster tool. Traditional methods to find planner optimizations were successful, but required extensive amounts of non-recurring engineering expense. This led to an exploration of how modern machine learning algorithms could be used to streamline the planner optimization process and find solutions beyond what a human engineer could discover.

Biography
Doug Suerich is Product Evangelist at The PEER Group Inc., the semiconductor industry’s leading supplier of factory automation software for smart manufacturing and Industry 4.0. Doug focuses on big data and remote connectivity solutions that help manufacturers collaborate securely on tools and data in production environments. A passionate advocate for smart manufacturing, Doug serves as an active member of the SEMI® Smart Manufacturing Advisory Council and SEMI SMART Manufacturing Technology Community, Americas Chapter.Doug has over 20 years of experience leading software teams for a variety of industries including semiconductor, manufacturing, and transportation. Most recently, he was involved in architecting PEER Group’s remote connectivity solution, Remicus™, and he was a champion in promoting the use of cloud computing and latest-generation web technologies.Prior to joining PEER Group, Doug was a software development manager, automation engineer, information systems specialist, and consultant. He has extensive experience designing and integrating robust automation software solutions. Doug holds a Bachelor of Applied Science with Honours in System Design Engineering from the University of Waterloo.