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Structural Decision Diagrams in Digital Test: Theory and Applications

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  • Дата: 1-02-2024, 04:57
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Название: Structural Decision Diagrams in Digital Test: Theory and Applications
Автор: Raimund Ubar, Jaan Raik, Maksim Jenihhin, Artur Jutman
Издательство: Birkhäuser/Springer
Серия: Computer Science Foundations and Applied Logic
Год: 2024
Страниц: 608
Язык: английский
Формат: pdf (true)
Размер: 12.4 MB

This is the first book that sums up test-related modeling of digital circuits and systems by a new structural-decision-diagrams model. The model represents structural and functional information jointly and opens a new area of research. The book introduces and discusses applications of two types of structural decision diagrams (DDs): low-level, structurally synthesized binary DDs (SSBDDs) and high-level DDs (HLDDs) that enable diagnostic modeling of complex digital circuits and systems.

In this book, we present a novel approach to modeling digital circuits and systems by graphs, constituting structural decision diagrams jointly representing structural and functional information about digital circuits and systems. The model allows multilevel and hierarchical representation to cope with the complexity of systems. The primary objective and the novelty of the new model is enabling uniform and formal reasoning of cause-effect diagnostic relations between the faults and behavior of circuit components to support digital test and verification solutions.

The complexity of digital systems is constantly growing and contributing to the phenomenon known as the “design gap”. At the same time, the nanometer-scale technology advancements impose new challenges to testing systems-on-chip. Shrinking geometries of devices and increasing the number of transistors produce new failure mechanisms, new manufacturing defect types, and reliability issues in electronic products. As a result, verification, test and diagnosis continue to dominate as crucial factors in time-to-market, dependability, and cost of modern many-core systems on chips.

An exciting question in the testing of today’s digital systems is: how to improve the reasoning of cause-effect relationships between errors, faults, and defects at the continuously growing complexities of systems? Two extreme trends can be observed in the diagnostic modeling of digital systems: defect-oriented and high-level fault modeling and test generation. To consider both trends simultaneously, hierarchical cross-level approaches are identified as a promising choice.

The difficulties in developing analytical high-level and hierarchical cross-level approaches to test generation and fault analysis are related to missing suitable tools and models. The register transfer level models, instruction set architectures, dataflow charts or hardware description languages (HDL, VHDL, Verilog, System C) are not well suited for formal cause-effect reasoning and test generation, forcing engineers to settle for heuristic and approximate methods, which, as a rule, do not provide satisfactory solutions.

Topics and features:

- Provides the definition, properties and techniques for synthesis, compression and optimization of SSBDDs and HLDDs
- Provides numerous working examples that illustrate the key points of the text
- Describes applications of SSBDDs and HLDDs for various electronic design automation (EDA) tasks, such as logic-level fault modeling and simulation, multi-valued simulation, timing-critical path identification, and test generation
- Discusses the advantages of the proposed model to traditional binary decision diagrams and other traditional design representations
- Combines SSBDDs with HLDDs for multi-level representation of digital systems for enabling hierarchical and cross-level solving of complex test-related tasks

This unique book is aimed at researchers working in the fields of Computer Science and computer engineering, focusing on test, diagnosis and dependability of digital systems. It can also serve as a reference for graduate- and advanced undergraduate-level computer engineering and electronics courses.

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