Bridging Design And Manufacturing Gap Through Machine Learning And Machine Generated Layout

Very-large-scale integrated (VLSI) circuits have entered the era of 1x nm technology node and beyond. Emerging manufacturing processes such as multiple patterning lithography, E-beam lithography (EBL), and selective etching, have been proposed to ensure nano-scale manufacturability. Meanwhile, design configurations keep updating in the pursuit of performance, design flexibility, and cost reduction. Despite such advancement in design and manufacturing, the closure of design flow becomes more and more challenging. The major issues come from three aspects: (1) expensive process modeling (e.g., complex lithography systems); (2) design-dependent manufacturability (e.g., yield sensitive to design patterns); (3) complicated design constraints (e.g., numerous placement and routing rules). To close the gap between design and manufacturing, automated layout generation requires cross-layer information feed-forward and feed-back, such as accurate process modeling and manufacturing-guided design optimization. This dissertation attempts to bridge the design and manufacturing gap through synergistic design optimization for automated layout generation and efficient machine learning techniques for lithography modeling. Our research includes manufacturing aware detailed placement, holistic post-layout optimization, and learning-based lithography modeling to achieve fast design and manufacturing closure. For manufacturing aware detailed placement, the limitation of conventional flow under the context of emerging lithography technologies and design configurations, e.g., MPL, EBL, and multiple-row height standard cells, is demonstrated. Then three important directions are explored with effective algorithms and new design flows: (1) triple patterning lithography (TPL) compliance for detailed placement considering both cross-row and intra-row decomposition conflicts; (2) simultaneous EBL stitch optimization with detailed placement; (3) multiple-row detailed placement for mixed-cell-height design. For post-layout optimization, given input placement and routing solutions, layouts need to be optimized for MPL, chemical mechanical polishing (CMP), and process variations, without affecting the functionality and performance of the designs. In particular, the following critical challenges are identified and resolved: (1) efficient and high-quality layout decomposition; (2) holistic dummy fill insertion to balance layout uniformity and coupling capacitance; (3) patterning aware design optimization for selective etching. The study focuses on yield improvement with manufacturing-guided layout manipulation and developing effective yet efficient approaches for even NP-hard problems such as layout decomposition. For lithography modeling, one of the major conflicts in modeling is considered: accuracy and amounts of calibration data. Models often rely on huge amounts of calibration data to achieve generality and high accuracy on a large variety of design patterns, while obtaining manufacturing data is usually expensive and time-consuming. With the observation of the potential correlation between datasets from consecutive technology nodes, a transfer learning scheme is proposed, leveraging existing data from an old technology node to help the calibration of the target technology node. Then an effective active learning algorithm with theoretical insights is also developed to actively select representative data for model calibration. With our machine learning techniques, a significant reduction on data is possible while maintaining high modeling accuracy. The effectiveness of proposed design optimization and machine learning techniques is demonstrated with extensive experiments on industrial-strength benchmarks. Our approaches are capable of reducing turn-around time, saving modeling costs, and enabling fast design and manufacturing closure.

The lithography process for chip manufacturing has been playing a critical role in keeping Moor's law alive. Even though the wavelength used for the process is bigger than actual device feature size, which makes it difficult to transfer layout patterns from the mask to wafer, lithographers have developed a various technique such as Resolution Enhancement Techniques (RETs), Multi-patterning, and Optical Proximity Correction (OPC) to overcome the sub-wavelength lithography gap. However, as feature size in chip design scales down further to a point where manufacturing constraints must be applied to early design phase before generating physical design layout. Design for Manufacturing (DFM) is not optional anymore these days. In terms of the lithography process, circuit designer should consider making their design as litho-friendly as possible. Lithography hotspot is a place where it is susceptible to have fatal pinching (open circuit) or bridging (short circuit) error due to poor printability of certain patterns in a design layout. To avoid undesirable patterns in layout, it is mandatory to find hotspots in early design stage. One way to find hotspots is to run lithography simulation on a layout. However, lithography simulation is too computationally expensive for full-chip design. Therefore, there have been suggestions such as pattern matching and machine learning (ML) technique for an alternative and practical hotspot detection method. Pattern matching is fast and accurate. Large hotspot pattern library is utilized to find hotspots. Its drawback is that it can not detect hotspots that are unseen before. On contrast, ML is effective to find previously unseen hotspots, but it may produce false positives. This research presents a novel geometric pattern matching methodology using edge driven dissected rectangles and litho award machine learning for hotspot detection.

The 2005 Virtual International Conference on IPROMS took place on the Internet between 4 and 15 July 2005. IPROMS 2005 was an outstanding success. During the Conference, some 4168 registered delegates and guests from 71 countries participated in the Conference, making it a truly global phenomenon. This book contains the Proceedings of IPROMS 2005. The 107 peer-reviewed technical papers presented at the Conference have been grouped into twelve sections, the last three featuring contributions selected for IPROMS 2005 by Special Sessions chairmen: - Collaborative and Responsive Manufacturing Systems- Concurrent Engineering- E-manufacturing, E-business and Virtual Enterprises- Intelligent Automation Systems- Intelligent Decision Support Systems- Intelligent Design Systems- Intelligent Planning and Scheduling Systems- Mechatronics- Reconfigurable Manufacturing Systems- Tangible Acoustic Interfaces (Tai Chi)- Innovative Production Machines and Systems- Intelligent and Competitive Manufacturing Engineering

Agile manufacturing is defined as the capability of surviving and prospering in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. Critical to successfully accomplishing AM are a few enabling technologies such as the standard for the exchange of products (STEP), concurrent engineering, virtual manufacturing, component-based hierarchical shop floor control system, information and communication infrastructure, etc. The scope of the book is to present the undergraduate and graduate students, senior managers and researchers in manufacturing systems design and management, industrial engineering and information technology with the conceptual and theoretical basis for the design and implementation of AMS. Also, the book focuses on broad policy directives and plans of agile manufacturing that guide the monitoring and evaluating the manufacturing strategies and their performance. A problem solving approach is taken throughout the book, emphasizing the context of agile manufacturing and the complexities to be addressed.

The 33 papers presented in this book were selected from amongst the 97 papers presented during the sixth edition of the International Conference on Integrated Design and Manufacturing in Mechanical Engineering during 28 sessions. This conference represents the state-of-the-art research in the field. Two keynote papers introduce the subject of the Conference and are followed by the different themes highlighted during the conference.

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Design has now become an important research topic in engineering and architecture. Design is one of the keystones to economic competitiveness and the fundamental precursor to manufacturing. The development of computational models founded on the artificial intelligence paradigm has provided an impetus for current design research. This volume contains contributions from the Second International Conference on Artificial Intelligence in Design held in June 1992 in Pittsburgh. They represent the state-of-the-art and the cutting edge of research and development in this field. They are of particular interest to researchers, developers and users of computer systems in design. This volume demonstrates both the breadth and depth of artificial intelligence in design and points the way forward for our understanding of design as a process and for the development of computer-based tools to aiddesigners.

This book gathers the peer-reviewed papers presented at the seventh edition of the international workshop "Service Orientation in Holonic and Multi-Agent Manufacturing - SOHOMA'17", held on October 19-20, 2017 and organized by the University of Nantes, France in collaboration with the CIMR Research Centre in Computer Integrated Manufacturing and Robotics at the University Politehnica of Bucharest, Romania, the LAMIH Laboratory of Industrial and Human Automation Control, Mechanical Engineering and Computer Science at the University of Valenciennes and Hainaut-Cambrésis, France and the CRAN Research Centre for Automatic Control, Nancy at the University of Lorraine, France. The main objective of SOHOMA'17 was to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions addressing trends in service orientation of agent-based control technologies with distributed intelligence. The book is organized in eight parts, each with a number of chapters describing research in current domains of the digital transformation in manufacturing and trends in future service and computing oriented manufacturing control: Part 1: Advanced Manufacturing Control, Part 2: Big Data Management, Part 3: Cyber-Physical Production Systems, Part 4: Cloud- and Cyber-Physical Systems for Smart and Sustainable Manufacturing, Part 5: Simulation for Physical Internet and Intelligent & Sustainable Logistics Systems, Part 6: Formal Methods and Advanced Scheduling for Future Industrial Systems, Part 7: Applications and Demonstrators, Part 8: Production and Logistic Control Systems. The contributions focus on how the digital transformation, such as the one advocated by "Industry 4.0" or "Industry of the future" concepts, can improve the maintainability and the sustainability of manufacturing processes, products, and logistics. Digital transformation relates to the interaction between the physical and informational worlds and is realized by virtualization of products, processes and resources managed as services.

A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.