AI Based Electronic Design Automation

An information processing system for designing semiconductor devices, comprising a first component that receives code and configuration data, a second component that realizes the semiconductor device based on the code and configuration data, and a third component that processes design history documents generated by the second component using a large-scale language model to provide proposals for modifying the code and configuration data.

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Training a machine learning system for automated design by using a dataset of high-level specifications and their corresponding design representations to enable early-stage optimization and size estimation. The trained system can then be used to determine a design representation for a given high-level specification, enabling efficient automated design processes.

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Automated design of safety-critical systems using High-Level Synthesis (HLS) with integrated safety features. The method modifies the initial high-level specification to incorporate safety constraints and functionality, enabling early consideration of safety requirements in the design process.

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Lately, architects have begun to favour a more result-oriented way of working based on artificial intelligence (AI) and its use for automation and software applications operation that support work with design data. The research aims to identify and systematise data on computer tools using artificial intelligence algorithms and the prospects for its development in architectural activities. The use of AI algorithms in architectural design is characteristic of all its stages, from the conceptual phase and basic design levels to detailing, development of design documentation, and future implementation, which is why various computer tools are available. The scientific paper describes three groups of AI software products. The first group includes software for architectural activities with AI-based functionality. The second group involves additional plug-ins with AI algorithms, i.e., independently compiled software modules installed and connected to the main programme, expanding its capabilities. The third group comprises online AI platforms, many of which are already available on the Inter... Read More

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A system for designing and testing electronic devices using artificial intelligence, comprising a first AI model that generates verification code from a product design, a second AI model that adjusts the design based on the verification code, and a verification engine that executes the verification code to generate a verifiability score. The system iteratively refines the design until the verifiability score meets a threshold, enabling automated verification and design optimization.

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This study explores the synergistic potential of integrating artificial intelligence (AI) with design thinking, aiming to enhance innovation and problem-solving capabilities in various domains. Beginning with a comprehensive examination of design thinking principles and methodologies, the research provides a foundational understanding of its core concepts. Subsequently, it investigates the capabilities of AI that can complement and augment the design thinking process. This includes an analysis of existing AI applications across different fields, highlighting their effectiveness and potential contributions to creative and strategic design processes. Central to the study is the integration of AI principles and tools within the framework of design thinking. Through a detailed review of AI techniques, the research identifies specific applications where AI can enhance ideation, prototyping, and user-centered design phases. Furthermore, it maps out how AI-driven solutions can be seamlessly incorporated into each stage of the design thinking process. This integration aims to optimize creati... Read More

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A system and method for predicting static voltage drop (SIR) violations in a clock tree synthesis (CTS) layout before routing, using machine learning models trained on CTS layouts with known SIR violations. The system receives CTS layout data, inspects each region, and compares it to the trained models to predict potential SIR violations. If no violations are predicted, the layout can be routed directly, avoiding the need for post-routing SIR analysis.

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Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the ... Read More

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Method for improving timing analysis to silicon correlation using machine learning to model physical parameter shifts between golden and existing models, generating a calibration database to calibrate static timing analysis (STA) behavior based on silicon data, and using the calibrated STA to generate updated timing data.

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Method for testing integrated circuit designs using machine learning techniques to select critical test cases. The method receives a plurality of test cases, generates an embedding data set, and clusters the test cases based on their characteristics. Critical test cases are selected from the clusters, and the integrated circuit is timed based on these critical test cases. The method can also use timing analysis data to select critical test cases.

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A neural network modeling method for semiconductor design that improves model consistency through active sampling and selective retraining. The method trains a regression model using simulation data, checks its consistency, and selectively collects additional data from regions where predictions fail. This targeted data collection enables efficient retraining of the model, improving its accuracy and reducing the need for manual intervention.

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A computer-aided design system for electronic circuits that uses artificial intelligence to automate the selection of components based on design specifications and performance requirements. The system receives a design specification, determines a set of performance ranges, and generates multiple solution variants by querying a database of components and applying AI algorithms to select components that meet the performance requirements. The system presents the solution variants in a graphical user interface, allowing users to visualize and compare the performance of different design options.

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A computer-aided design (CAD) system that incorporates an artificial intelligence advisor to inform users of potential hardware limitations and constraints during the design process. The AI advisor analyzes the design and identifies potential issues based on a knowledge graph constructed from project archives, expert knowledge, and industry standards. It generates recommendations to prevent hardware damage and ensures safe operation, providing real-time feedback to the user through the CAD interface.

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Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made on Artificial Intelligence (AI) techniques, questions arise for the future of MDE such as how existing MDE techniques and technologies can be improved or how other activities which currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in the medium and long term perspective.

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Automated design of physical systems using generative models. The system trains a generative model to output initial system topologies based on functional and behavior requirements. The model is trained on a large dataset of system diagrams, such as process flow diagrams, from which topology information is extracted and converted into a formal language representation. The trained model generates a topology for a to-be-designed system, which is then instantiated with components and their parameters through optimization techniques.

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In the context of digitalization, the continuous progress of science and technology and the increase in the degree of informationization, the traditional digital design means are more and more unable to meet the needs of today's diversified design, so the addition of artificial intelligence technology has also become an irreversible trend in the design means. As a powerful tool and technical means, artificial intelligence can largely improve the quality and efficiency of design, and can form a knowledge base and experience base through the accumulation of a large amount of data to provide reference and guidance for subsequent design work. Through the practice of AI in the field of design in recent years, the article combs and summarizes its excellent examples, focuses on the pioneering and mining of AI-driven design innovation, and thus leads to the discussion of the future development of intelligent design.

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Rapid advances in artificial intelligence (AI) and machine learning have had a significant impact on various fields, including electronic design automation (EDA). This study aims to compare current EDA routing algorithms with AI-based routing algorithms, highlighting their respective advantages and limitations. Through a comprehensive analysis of existing EDA routing algorithms and artificial intelligence-based technologies, this study explores the applicability of these algorithms in different EDA scenarios, with a focus on their effectiveness and efficiency in routing tasks, and aims to compare and contrast traditional and AI-based routing algorithms in the context of electronic design automation. It also shows that the current AI technology will play an important role in the future development of EDA software. Therefore, the combination of AI technology may be the focus of EDA software development, so as to help elucidate the evolving landscape of EDA and provide insights into the potential future direction, and provide a a comprehensive understanding of their underlying methodolo... Read More

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A method for automatic routing of electronic circuits that decouples routing grids from core algorithms, enabling efficient handling of complex design rule checking (DRC) rules from various foundries. The method generates a routing graph based on DRC rules and dynamically updates it in real-time as the user creates routing segments and vias, allowing for fast and accurate routing of complex digital and mixed-signal designs.

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A method for predicting circuit design performance and identifying optimization opportunities using machine learning models. The method determines a set of circuit design features, applies a predictive model to estimate performance metrics, and an explanation model to quantify feature contributions. It then selects the most impactful feature and retrieves a corresponding optimization recipe from a database, enabling targeted design improvements.

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A method for generating a semiconductor device layout with improved accuracy and reduced computation, comprising: receiving a first layout; performing machine learning-based process proximity correction (PPC) to generate a second layout; and performing optical proximity correction (OPC) on the second layout to generate a third layout.

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