Machine Learning in Nanoelectronics : Devices, Circuits and Systems.

Format:

Book

Author/Creator:

Maurya, Ashish.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Physical Description:

1 online resource (481 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

Bridge the gap between advanced algorithms and hardware innovation with this essential book, which details how machine learning is being used to overcome challenges in nanoelectronics while laying the critical groundwork for the future of neuromorphic computing hardware.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Chapter 1 Introduction to Machine Learning in Nanoelectronics

1.1 Introduction

1.1.1 The Need for Advanced Modeling in Nanoelectronics

1.1.2 Scope of Machine Learning Applications in Semiconductors

1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale

1.2.1 Moore's Law, Transistor Scaling Challenges

1.2.2 Physical Scaling Limits in Nanoscale Devices

1.2.3 Various Nanoscale Device Technologies

1.2.4 Machine Learning's Role in Overcoming Scaling Barriers

1.3 Machine Learning in Nanoscale Device Simulation

1.3.1 Traditional Simulation Techniques

1.3.1.1 Drift-Diffusion Model (DDM)

1.3.1.2 Monte Carlo (MC) Simulations

1.3.1.3 Non-Equilibrium Green's Function (NEGF) Method

1.3.1.4 Molecular Dynamics (MD)

1.3.1.5 Quantum Mechanical Models: Density Functional Theory (DFT) and Tight-Binding (TB) Models

1.3.2 Surrogate Modeling for Device Behaviour

1.3.2.1 Acceleration of Quantum Simulations

1.3.2.2 Design Space Exploration and Optimization

1.3.2.3 Handling Variability and Defects

1.3.2.4 Transfer Learning for New Materials and Devices

1.3.2.5 Real-Time Parameter Tuning

1.4 Process Optimization in Semiconductor Manufacturing

1.4.1 Variability and Yield in Nanoscale Manufacturing

1.4.2 Real-Time Process Control with ML

1.4.3 Case Study: Graph-Based Yield Prediction in IC Manufacturing

1.4.4 Reliability, Fault Detection and Self-Heating Systems

1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design

1.5.1 Device Structure

1.5.2 Machine Learning Approach

1.5.3 Design Space Exploration

1.5.4 Predictive Modeling

1.5.5 Process Variation Mitigation

1.6 Future Directions and Challenges

1.7 Conclusion

Summary

References.

Chapter 2 Machine Learning to Explore Opportunities in Quantum

2.1 Introduction to Quantum Opportunities

2.2 Understanding Quantum Data

2.3 Machine Learning Techniques for Quantum Applications

2.4 Case Studies and Applications

2.5 Tools and Frameworks for Implementation

2.6 Challenges and Opportunities in QML

2.7 Conclusion

References

Chapter 3 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review

3.1 Introduction

3.2 Predictive Modelling and Machine Learning's Application in Cancer Diagnostics

3.2.1 Diagnosis of Cancer

3.2.2 Treatment Planning

3.3 Customized Medical Care

3.3.1 Overview of Machine Learning in Healthcare

3.3.2 Machine Learning Applications in Cancer Therapy

3.3.3 Nanotechnology Applications in Cancer Therapy

3.4 Result and Future Perspective

Chapter 4 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis

4.1 Introduction

4.2 Methodology

4.3 Simulation Results

4.4 Conclusion

Chapter 5 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators

5.1 Introduction

5.2 Computational Complexities of Convolutional Neural Networks

5.3 Evolution of CNN Accelerators

5.4 Model Compression Approaches

5.5 Hardware Optimization Techniques

5.6 Design Space Exploration

5.7 Hardware Platforms for Implementing CNNs

5.8 Sparse Neural Networks

5.9 Future Scope and Summary

Chapter 6 Flexible Energy Storage Devices

6.1 Introduction

6.1.1 Flexible Devices

6.1.2 History and Origins of Flexible Devices

6.1.3 The Evolution of Flexible Devices

6.2 Energy Storage

6.2.1 Energy Storage Technologies and Their History

6.2.1.1 Batteries

6.2.1.2 Supercapacitor Storage Systems (SSSs).

6.3 Criteria for a Device to Store Energy

6.3.1 The Critical Role of Energy Storage in Modern Energy Systems

6.4 Need of Flexible Energy Storage Devices

6.4.1 Advantages of Flexible Energy Storage Devices

6.4.2 Disadvantages of Flexible Energy Storage Devices

6.5 Different Structures That are Being Used in Flexible Energy Storage

6.5.1 Fiber Structures

6.5.2 Island Bridge Structure

6.5.3 Interdigital Structure

6.6 Emergence of Micro-Supercapacitors

6.7 Materials for Energy Storage Devices

6.8 Electrode Materials

6.8.1 Carbon-Based Electrode

6.8.2 Graphene-Based Flexible Electrodes

6.9 Comparison Sheet of Different Materials

Chapter 7 VLSI Design for AI Applications

7.1 Introduction

7.2 Specialized Neural Networks Accelerators

7.3 Memory Hierarchy Optimization

7.4 High Speed Interconnects

7.5 Power Optimization

7.6 Scalability

7.7 Key Components of VLSI Design for AI

7.7.1 Field Programmable Gate Array (FPGA)

7.7.2 Application-Specific Integrated Circuit (ASIC)

7.8 Accelerating Chip Design Using ML

7.9 Future Trends in VLSI Design for AI

7.10 Industrial Application of VLSI Design

Chapter 8 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale

8.1 Introduction

8.2 Adiabatic Charging Principle

8.3 Adiabatic Logic Family

8.4 Comparative Simulation Results

8.5 Key Challenges

8.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic

Chapter 9 High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities

9.1 Introduction

9.2 Antenna Design Equations

9.3 Design and Simulation

9.4 Conclusions

Chapter 10 Layout Dependent Effects.

10.1 Overview of Layout Considerations

10.1.1 Design Rules

10.2 Analog Layout Techniques

10.2.1 Multifinger Transistors

10.2.2 Symmetry

10.2.3 Shallow Trench Isolation Issues

10.3 Effects of Layout in Deep Nanoscale CMOS

10.3.1 Types of LDEs

10.4 Mismatch of Devices

10.4.1 Impact of Mismatch

10.4.2 Types of Matching

10.4.3 Advantages and Limitations of CC

Chapter 11 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications

11.1 Introduction

11.2 Digital Filtering Techniques

11.3 Hardware Architecture

11.3.1 Direct Form and Transposed Form

11.3.2 Hardware Analysis of an FIR Filter

11.3.3 Adder Logic

11.3.4 Multiplier Technique

11.3.5 Multiplier-Accumulator (MAC) Unit

11.3.6 FIR Filter Design without Using Multiplier

11.4 Simulation Setup and Results Analysis

11.5 Summary

Chapter 12 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications

12.1 Introduction

12.2 Neural Network Architectures

12.3 Deep Learning Algorithms for Medical Images

12.4 Recent Trends in Hardware Architectures of DNN

12.5 Challenges and Opportunities

12.6 Summary

Acknowledgements

Chapter 13 Integration with IoT for Smart Homes

13.1 Introduction

13.2 Sensors for Smart Homes

13.2.1 Motion Detection

13.2.2 Flame-Gas Detection Sensor

13.2.3 Toxic Gas Detection

13.2.4 Moisture Leak Detection

13.2.5 Proximity Sensors

13.2.6 Temperature Sensors

13.2.7 Humidity Sensors

13.2.8 Light Sensors

13.2.9 Smart Thermostat Sensor

13.2.10 Intercom/Hub

13.3 Connectivity Protocols for IoT Smart Homes

13.3.1 Zigbee

13.3.2 Z-Wave

13.3.3 Wi-Fi

13.3.4 Bluetooth and Bluetooth Low Energy (BLE)

13.3.5 MQTT (Message Queuing Telemetry Transport).

13.3.6 CoAP (Constrained Application Protocol)

13.3.7 LoRa WAN (Long Range Wide Area Network)

13.3.8 NFC (Near Field Communication)

13.3.9 Cellular(4G/5G)

13.4 Smart Appliances for Smart Homes

13.4.1 Smart Kitchen Appliances

13.4.2 Smart Laundry Appliances

13.4.3 Smart Cleaning Devices

13.4.4 Smart Security Devices

13.4.5 Smart Lighting

13.4.6 Smart Speaker and Hubs

13.4.7 Smart Energy Monitors

13.4.8 Integration and Automation

13.4.9 Benefits of Smart Devices

13.5 Voice Assistants

13.5.1 Amazon Alexa

13.5.2 Google Assistant

13.5.3 Apple Siri

13.5.4 Microsoft Cortana

13.5.5 Samsung Bixby

13.5.6 Raspberry Pi and Custom Assistants

13.6 Security and Surveillance

13.7 Home Healthcare System

13.7.1 Features for Healthcare in Smart Home

13.7.2 User Safety

13.7.3 Patient Health

13.7.4 Design Flexibility

13.7.5 Information and User Engagement

13.8 User Interfaces and Experiences

13.8.1 Mobile Apps and Dashboards

13.8.2 Wearable and Voice Interaction

13.8.3 Intuitive Design for Usability

13.8.4 Remote and In-Home Control Panels

13.9 Sustainability and Smart Homes

13.9.1 Energy Management

13.9.2 Sustainable Appliances

13.9.3 Smart Grids and Renewable Integration

13.9.4 Automated Water and Climate Control

13.10 Future Trends in Smart Home IoT

13.10.1 AI and Machine Learning

13.10.2 Edge Computing

13.10.3 5G and the Future of Connectivity

13.10.4 Interoperability and Universal Standards

13.10.5 Sustainability and Green Energy Solutions

13.11 Conclusions

About the Editors

Index

Also of Interest

EULA.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-33620-9

1-394-33619-5

9781394336197

OCLC:

1578879815