オランダ半導体製造AI視察登録フォーム F.xlsx

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1	AI for Semiconductor Manufacturing オランダ視察参加フォーム　※必ずダウンロード後に入力してください。
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3	参加者氏名（漢字）
4	氏名（ローマ字）
5	Department
6	Title
7	Email
8	個別ミーティング希望
9	Precision Week参加希望
10	※視察の定員を超えた場合には、参加人数についてご調整をお願いする場合がございます。
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12	本フォームの送付をもって参加を受け付けます。※下記メールアドレスの[a]を@と入れ替えてください。
13	オランダ大使館村上武	takeshi.murakami[a]hollandinnovation.jp
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15	御社のご興味について教えてください。オランダのソリューションプロバイダーとの個別1on1ミーティングのアレンジに使用します。ご入力いただいた内容は先方企業とのみ共有されます。オレンジのコラムにご興味の技術に(Yes)を入れ、自社ニーズを共有したミーティングを希望の場合には、ニーズの概要をご記入ください。
16	Company / Project	Process	Challenge	Solution	Presentation セミナーのプレゼンで扱って欲しい	1on1 (30 min) 個別ミーティング	1on1 Share our needs (60 min) 自社のニーズを共有	1on1 Describe your Needs 自社ニーズの概要
17	Company A	Front-end	Process and equipment parameter optimization with limited iterations	Recipe Builder creates AI models of manufacturing processes and applies advanced optimization to optimize process and machine parameters in a limited number of iterations.
18		Back-end	Bonding-process alignment optimization	Develops algorithms to optimize alignment during bonding processes for advanced-packaging applications.
19			Optimization of advanced-packaging process and equipment parameters	Explores and develops the application of Recipe Builder, Model Predictive Control, and related optimization approaches to advanced-packaging process control.
20		Metrology / Inspection	Inference of physical properties and structural information from metrology data	Develops inverse AI models that reconstruct samples from measurement data, as well as approaches combining AI surrogate models with optimization, including applications in holography and EDS/EDX.
21			AI-based defect detection and classification	Applies AI algorithms to inspection images for automated defect identification and classification.
22		Equipment Engineering / Control	Functional and structural design and performance optimization of complex semiconductor equipment	Combines high-tech equipment domain knowledge with advanced algorithms for equipment development and optimization.
23			Fast prediction using computationally intensive physics-based models	Builds AI surrogate models that mimic physics-based simulations. Reporting speed improvements of more than 100× in applications involving fluid dynamics, electromagnetics, and thermo-mechanical models.
24			Equipment setpoint optimization	Optimizes control-parameter setpoints to enable more efficient and faster operation using the same hardware.
25			More efficient equipment calibration and tuning with reduced operator dependency	Provides tools that guide operators through calibration and tuning processes, reducing completion time and dependence on individual operator experience.
26			Understanding legacy software and complex equipment architectures	Uses Generative AI for Greenfield software development and for improving the maintainability of large existing codebases.
27		Equipment Maintenance	Early failure prediction and Predictive Maintenance	Combines machine sensor data and physics knowledge to detect deviations from expected physical behaviour and identify failures at an early stage. Reporting practical examples, including semiconductor applications.
28			Fault detection when conventional sensor data is insufficient	Uses ultrasonic microphones and analysis of sound patterns and frequency characteristics to detect faults and investigate potential root causes.
29		Manufacturing Data / Cross-process AI	Problem analysis and Root Cause Analysis using large volumes of manufacturing data	Develops industrial chatbots combining machine sensor data, technical documentation, and other information sources, together with Root Cause Analysis, diagnostics, and anomaly detection. This is presented as a general manufacturing capability rather than a semiconductor-specific solution.
30			AI use without moving confidential data outside the machine or factory	Supports local algorithm deployment so sensitive data does not need to leave the factory or machine environment; Edge AI is also used.
31			Modelling relationships among process, material, and equipment information	Recipe Builder develops models describing relationships among process parameters, material properties, and equipment characteristics, with the aim of optimizing the production chain.
32	Company B	Back-end	Wafer alignment for advanced packaging	Uses ML-based fiducial detection based on GDS design information to determine wafer position and orientation with sub-pixel accuracy.
33		Metrology / Inspection	Inference of physical properties and structural information from metrology data	Uses high-magnification microscopy and ML to associate imaging outputs with physical properties such as grating-coupler fill factor. Applications to resist thickness and interconnect impedance are work in progress.
34			Wafer warpage measurement and focus control in high-magnification microscopy	Uses high-speed autofocus sensing to measure wafer warpage and offset scanning to image structures at different heights or beneath transparent layers.
35			Development of nanoscale particle-detection techniques	Develops a hardware/software solution using Fourier Ptychography for particle detection in the 10–100 nm range. Work in progress.
36			Development of small-height-variation measurement techniques	Investigates high-speed autofocus sensing for pick-and-place applications, targeting distance variations as small as approximately 20 nm. Work in progress.
37			AI-based defect detection and classification	Provides an ML suite for annotation, training, and deployment of algorithms for offline and inline defect inspection.
38			Inspection combining microscopy, SEM, and GDS data	Overlays microscopy and SEM images and combines them with wafer-design information to create layered defect maps and support inspection-workflow optimization.
39	Company C	Equipment Engineering / Control	Long engineering and validation cycles for complex semiconductor and high-tech equipment	Develops AI-assisted engineering using digital twins, Virtual V&V, generative mechanics, requirements analysis, and automated test generation to shorten engineering iterations and connect requirements, design, simulation, testing, and manufacturing feedback. Currently a strategic development area.
40			Increasing engineering workload for equipment design, verification, and test development	Applies AI to schematic review, component selection and layout support, generative CAD with manufacturing constraints, DFM checks, and automatic generation of test cases and production tests, while keeping final engineering validation with domain experts.
41		Metrology / Inspection	AI-based defect detection and classification in manufacturing inspection	Provides an established AI Framework for automatic visual inspection, including dataset creation, reusable inspection tasks, anomaly detection, inference orchestration, execution planning, and explainability of failed-product predictions.
42		Manufacturing Data / Cross-process AI	Manufacturing data distributed across MES, machines, images, logs, and test systems is difficult to use systematically for analytics and AI	Builds a manufacturing data platform combining scalable MES data, a data lake, bronze-silver-gold data structures, semantic layers, centralized image/log storage, and real-time data ingestion to support analytics and future AI applications.
43			Identification of yield loss, failures, defects, and production issues from large volumes of manufacturing data	Uses manufacturing analytics to analyze machine cycles, alarms, disturbances, yield, failures, defects, scrap, test quality, and other production indicators, with further AI-assisted decision-making and automated process validation on the roadmap.
44		Equipment Maintenance	Detection of anomalies and emerging failures from equipment and test data	Investigates anomaly detection in test results and analysis of historical measurement data, initially including flying-probe test data; predictive reliability and machine-health monitoring are also included in the manufacturing roadmap.
45	Company D	Metrology / Inspection	3D reconstruction from a limited number of electron-microscopy projections	Develops physics-informed AI to reconstruct buried 3D semiconductor structures using approximately seven projection images instead of the conventional 60–120.
46	Company E	Metrology / Inspection	Acquisition, processing, and industrialization of large-scale 4D-STEM data	Integrates acquisition, DSP, calibration, and large-scale diffraction-data pipelines for 4D-STEM, supporting the productization of scientific detector technology into an industrial platform.
47			AI-based defect detection and classification	Provides a visual-inspection platform integrating cameras, AI models, and processing infrastructure for object detection and defect classification. The platform is applied across multiple industries, including semiconductor.
48		Equipment Maintenance	Early failure prediction and Predictive Maintenance	In a semiconductor lithography-machine case, combines real-time monitoring, signal processing, and AI-based anomaly-pattern recognition to predict limiter-pin failures.
49			Continuous monitoring without impacting machine performance	Uses zero-impact workload scheduling to perform continuous diagnostic monitoring within critical machine-driver architecture.
50	Company F	Equipment Engineering / Control	Delay in starting AI development because real-world data required for AI development cannot be collected until the equipment prototype is completed.	By developing AI and hardware in parallel, the overall development time can be significantly reduced. Hardware design decisions can also be made before the equipment is completed, based on their impact on defect detection performance.
51	Holland Hitechプロジェクト（以下はコラボレーションの対象とはなりません）
52	FedDepo	Front-end	Deposition process prediction, optimization, and model adaptation to changing conditions	Uses time-series data from deposition equipment and wafers for forecasting, anomaly prediction, continual model updating, and deposition-process optimization.
53			AI model training without directly sharing confidential manufacturing data	Develops Federated Learning methods that allow AI models to be trained without directly exchanging confidential manufacturing data.
54		Manufacturing Data / Cross-process AI	Time-series AI models that adapt to changing manufacturing conditions	Researches time-series forecasting, synthetic-data modelling, Federated Learning, and continual model updating.
55	DELTAS	Front-end	Lithography mask optimization under process variations and stochastic effects	Uses deep generative models for inverse lithography to optimize mask designs that remain robust against process variations and stochastic effects.
56			Reconstruction of subsurface wafer structures from noisy SEM images	Combines physical models with Deep Learning to reconstruct subsurface wafer structures from noisy SEM images.
57	N-HIPO	Back-end	Optimization of mechatronic conditions and bonding quality in Hybrid Bonding	Develops a Digital Twin combining physics-based models and data-driven AI to optimize mechatronic conditions and bonding quality in Hybrid Bonding. TU/e and ASMPT ALSI will validate the technologies using industrial equipment.
58			Optimization of thermal control in Thermo-Compression Bonding	Applies the same physics-based and data-driven Digital Twin approach to thermal-control optimization in Thermo-Compression Bonding.
59	SBIMetrology	Metrology / Inspection	Inference of physical properties and structural information from metrology data	Uses simulation-based inference and data-driven modelling to advance semiconductor imaging, wafer metrology, and uncertainty quantification.
60			Optimization of optical measurement conditions and experimental design	Uses simulation-based inference and data-driven modelling to improve the experimental design of optical measurement conditions.
61	HDA	Equipment Engineering / Control	Functional and structural design and performance optimization of complex semiconductor equipment	Applies AI and design automation to functional design, structural design, and performance optimization of semiconductor manufacturing equipment.
62			Understanding legacy software and complex equipment architectures	Applies AI to support understanding of legacy software and complex semiconductor-equipment architectures.
63			Application of AI to equipment diagnostics and supervisory control	Applies AI and design automation to equipment diagnostics and supervisory control. The source does not explicitly claim real-time control.
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66	その他のニーズ
67	上記に含まれないニーズがありましたら、企業名を指定して御社のニーズをご入力ください。
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