Our book provides a succinct summary of Artificial Intelligence applications across multiple Very-Large-Scale Integration (VLSI) design flow and testing domains. One technique of fabrication, CMOS technology, is where the evolution of VLSI begins. Complementary Metal Oxide Semiconductor (CMOS) offers numerous advantages but to pack high number of Integrated Circuits (ICs) in compact geometries, VLSI enters into picture. We can design VLSI using Computer Aided Tools like Electronic Design Automation (EDA). VLSI design consists of two primary parts: the front end and the back end. Fabrication procedures such as Architectural Design, Gate Level Design, Simulation, Hardware Descriptive Language (HDL), Circuit Level Design, Verification, and Fabrication are included at the back end. Descriptive languages for hardware are the main emphasis of front end. HDL is essential to system design use because it enables the behavior of the core system to be verified and modelled prior to the design being converted by synthesis tools into physical hardware, such as gates and wires. Different chemical and physical processes are needed for the backend operations. It is possible to create Artificial Intelligence algorithms that aid in raising the yield of physical processes. Artificial Intelligence and Machine Learning (AI-ML) algorithms assist in predicting parametric yield at the device level. Furthermore AI-ML algorithms are implemented at the gate level to analyse the total latency of the gate connection. Also, AI-ML algorithms can be used at the circuit level to maximize the performance of circuits. A combination of AI-ML algorithms in EDA tools allows for the examination of propagation delay, power leakage, etc. During lithography, transfer learning can be used to produce data. Effective testing becomes a crucial task as electronic systems and components becoming more complex, striking a balance between the need for high quality and cost effectiveness. In spite of its labour-intensive nature, testing has become a critical technique for cutting total costs in the production of electronic systems, boards, and VLSI chips. It plays a critical role in ensuring that VLSI design satisfy quality requirement by identifying circuit faults. In order to handle the complexity of contemporary systems, recent developments have incorporated machine learning (ML) and artificial intelligence (AI) into established VLSI testing techniques.
Table of Contents
1 Evolution of Very-Large-Scale Integration (VLSI)
1.1 Introduction
1.2 Complementary Metal Oxide Semiconductor (CMOS)
1.2.1 CMOS Technology
1.2.2 Advantages of CMOS Technology
1.3 Why Very-Large-Scale Integration (VLSI)?
1.4 Electronic Design Automation (EDA)
1.5 Conclusion
2 VLSI Design Flow
2.1 Introduction
2.2 Design Specification
2.3 Architecture Design Level
2.4 Gate Level Design
2.5 Circuit Level Design
2.6 Hardware Descriptive Language (HDL) Coding
2.7 Circuit Simulation and Analysis
2.8 Fabrication
2.9 Conclusion
3 Use of AI in VLSI Design Flow
3.1 Introduction
3.2 Artificial Intelligence at Design Level
3.3 Artificial Intelligence at Gate Level
3.4 Artificial Intelligence at Circuit Level
3.5 Artificial Intelligence at Resistor Transistor Logic (RTL) Level
3.6 Artificial Intelligence at Post Layout Simulation
3.7 Artificial Intelligence in Manufacturing
3.7.1 Artificial Intelligence for Lithography
3.8 Conclusion
4 VLSI Testing
4.1 Introduction
4.2 Testing Techniques
4.2.1 Design for Testability (DFT)
4.2.2 Automatic Test Pattern Generation (ATPG)
4.2.3 Built-in-Self-Test (BIST)
4.3 Very-Large-Scale Integration (VLSI) Testing Challenges
4.4 Enhancing Fault Detection with Machine Learning
4.5 Upcoming Prospect of Machine Learning in Very-Large-Scale Integration (VLSI)
4.6 Conclusion
5 Role of AI in SoC Design
5.1 Introduction
5.2 Design Elements of SoC
5.3 Combination of AI and SoC
5.3.1 Custom Hardware Acceleration
5.3.2 Optimizing Memory
5.3.3 Optimizing Power
5.3.4 Security
5.3.5 Connection of Data in Real Time
5.4 High Level Architecture of an System on Chip (SoC) Design
5.5 Conclusion
6 Summary
Objectives and Topics
This book explores the integration of Artificial Intelligence and Machine Learning techniques into the VLSI design flow and testing domains. The primary objective is to demonstrate how AI can optimize the design cycle, enhance parametric yield, and improve testing efficiency for complex semiconductor systems.
- Evolution of VLSI and CMOS technology
- VLSI design flow: From specification to fabrication
- AI-driven optimization in circuit design and layout
- Advanced testing methodologies (DFT, ATPG, BIST) enhanced by machine learning
- Role of AI in System-on-Chip (SoC) architecture and optimization
Excerpt from the Book
3.6 AI at Post Layout Simulation
Utilizing training data available prior to the engineering change order (ECO) modification, the learning-based approach can be expanded to anticipate the dynamic IR drop and ground bounce during the ECO modification.[1] By using Ohm's law (V=IR), we may define IR drop as the potential difference between the two terminals of a conducting wire during current flow. Chips utilize metal networks, which make up their power delivery networks (PDNs), to distribute power and ground. Ohm's law states that when current passes through the PDN, some of the voltage across the network is dropped. The term "IR drop" describes this phenomenon. [37] An integrated circuit's output voltage is measured in relation to the chip's ground. As a result, in a system, a small quantity of inductance is added to the circuit by the small wire connect that connects the chip to the package's lead frame. A spike in current passes through this inductance when Vout drops, producing a spike in voltage. A device attached to the circuit's output presents a logical zero. We refer to this as "ground bounce." [38]
As previously reported, multiple statistical/parameter estimations, such as the leakage power, entire power, transmission delay, effects induced by aging, yield, and power consumption, can be addressed by integrating AI and ML into EDA tools and methodologies at different stages of circuit simulation. The discipline of CAD-VLSI will undergo a revolution when automated learning methodologies are integrated into VLSI circuit design and simulation, owing to the many associated benefits.[1]
Summary of Chapters
1 Evolution of Very-Large-Scale Integration (VLSI): Describes the transition of the electronics industry sparked by CMOS technology and the necessity of VLSI in managing increasing chip complexity.
2 VLSI Design Flow: Outlines the sequential stages of the front-end and back-end design process, including specification, gate-level design, and fabrication procedures.
3 Use of AI in VLSI Design Flow: Explores how AI and machine learning models are applied across different abstraction levels, from device and gate level to post-layout simulation and lithography.
4 VLSI Testing: Discusses the integration of AI into fundamental testing methodologies to improve fault detection and handle the rising complexity of modern electronic systems.
5 Role of AI in SoC Design: Examines how Artificial Intelligence improves SoC architectures by optimizing memory, power, and security while facilitating hardware acceleration.
6 Summary: Concludes the work by synthesizing the key advancements in VLSI design and testing achieved through the implementation of AI and ML technologies.
Keywords
Complementary Metal Oxide Semiconductor, Electronic Design Automation, Neural Networks, Optical proximity correction, Logistic regression, Automatic Test Pattern Generation, Design for Testability, Built-in-Self-Test, System on Chip, Machine Learning, Artificial Intelligence, VLSI, CMOS, Lithography, SoC Architecture.
Frequently Asked Questions
What is the core focus of this publication?
The book focuses on the intersection of Artificial Intelligence (AI) and Machine Learning (ML) with Very-Large-Scale Integration (VLSI) design and testing processes.
What are the primary thematic areas covered?
The central themes include the evolution of VLSI through CMOS, the conventional VLSI design flow, the application of AI in design optimization, advancements in testing techniques, and the role of AI in SoC architecture.
What is the main objective of the authors?
The primary goal is to provide a comprehensive summary of how AI models like Neural Networks and Transfer Learning can streamline VLSI workflows, reduce design time, and increase manufacturing yield.
Which scientific methods are analyzed in this book?
The book reviews methodologies such as the Monte Carlo method, Support Vector Regression (SVR), CNNs, ANNs, and various testing frameworks like DFT, ATPG, and BIST.
What does the book cover in its main body?
The main body details the integration of AI across design stages (from device level to manufacturing) and examines the enhancement of fault detection and System-on-Chip (SoC) performance.
Which keywords characterize this work?
Key terms include CMOS, EDA, SoC, Machine Learning, VLSI, Design for Testability (DFT), and Lithography processes.
How does AI specifically help with IR drop and ground bounce?
AI models utilize training data to anticipate dynamic IR drop and ground bounce during engineering change order (ECO) modifications, allowing for preemptive analysis that surpasses traditional rule-based tools.
What is the significance of the proposed AI-based System-on-Chip (SoC) architecture?
The proposed architecture incorporates Smart Intelligence Clusters and specialized DSP cores to deliver high performance, energy efficiency, and security for data-intensive AI applications.
What role does Transfer Learning play in lithography?
Transfer learning addresses the scarcity of training data in lithography by combining knowledge from previous technology nodes with data produced by current processes to efficiently train new models.
- Quote paper
- Hardik Modi (Author), Vanshika Jain (Author), Diya Gangurde (Author), Dharmendra Chauhan (Author), Sagarkumar Patel (Author), 2024, Applications of Artificial Intelligence in Physical Design and Testing of Very Large Scale Integration, Munich, GRIN Verlag, https://www.grin.com/document/1509759
