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Betty Prince og David Prince
(2018)
John Wiley & Sons
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Memories for the Intelligent Internet of Things
Betty Prince og David Prince
(2018)
Sprog: Engelsk
John Wiley & Sons, Incorporated
1.554,00 kr.
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Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: John Wiley & Sons (April 2018)
- Forfattere: Betty Prince og David Prince
- ISBN: 9781119298953
A detailed, practical review of state-of-the-art implementations of memory in IoT hardware As the Internet of Things (IoT) technology continues to evolve and become increasingly common across an array of specialized and consumer product applications, the demand on engineers to design new generations of flexible, low-cost, low power embedded memories into IoT hardware becomes ever greater. This book helps them meet that demand. Coauthored by a leading international expert and multiple patent holder, this book gets engineers up to speed on state-of-the-art implementations of memory in IoT hardware. Memories for the Intelligent Internet of Things covers an array of common and cutting-edge IoT embedded memory implementations. Ultra-low-power memories for IoT devices-including plastic and polymer circuitry for specialized applications, such as medical electronics-are described. The authors explore microcontrollers with embedded memory used for smart control of a multitude of Internet devices. They also consider neuromorphic memories made in Ferroelectric RAM (FeRAM), Resistance RAM (ReRAM), and Magnetic RAM (MRAM) technologies to implement artificial intelligence (AI) for the collection, processing, and presentation of large quantities of data generated by IoT hardware. Throughout the focus is on memory technologies which are complementary metal oxide semiconductor (CMOS) compatible, including embedded floating gate and charge trapping EEPROM/Flash along with FeRAMS, FeFETs, MRAMs and ReRAMs. Provides a timely, highly practical look at state-of-the-art IoT memory implementations for an array of product applications Synthesizes basic science with original analysis of memory technologies for Internet of Things (IoT) based on the authors' extensive experience in the field Focuses on practical and timely applications throughout Features numerous illustrations, tables, application requirements, and photographs Considers memory related security issues in IoT devices Memories for the Intelligent Internet of Things is a valuable working resource for electrical engineers and engineering managers working in the electronics system and semiconductor industries. It is also an indispensable reference/text for graduate and advanced undergraduate students interested in the latest developments in integrated circuit devices and systems.
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Detaljer om varen
- Hardback: 344 sider
- Udgiver: John Wiley & Sons, Incorporated (Juni 2018)
- Forfattere: Betty Prince og David Prince
- ISBN: 9781119296355
A detailed, practical review of state-of-the-art implementations of memory in IoT hardware
As the Internet of Things (IoT) technology continues to evolve and become increasingly common across an array of specialized and consumer product applications, the demand on engineers to design new generations of flexible, low-cost, low power embedded memories into IoT hardware becomes ever greater. This book helps them meet that demand. Coauthored by a leading international expert and multiple patent holder, this book gets engineers up to speed on state-of-the-art implementations of memory in IoT hardware.
Memories for the Intelligent Internet of Things covers an array of common and cutting-edge IoT embedded memory implementations. Ultra-low-power memories for IoT devices-including plastic and polymer circuitry for specialized applications, such as medical electronics-are described. The authors explore microcontrollers with embedded memory used for smart control of a multitude of Internet devices. They also consider neuromorphic memories made in Ferroelectric RAM (FeRAM), Resistance RAM (ReRAM), and Magnetic RAM (MRAM) technologies to implement artificial intelligence (AI) for the collection, processing, and presentation of large quantities of data generated by IoT hardware. Throughout the focus is on memory technologies which are complementary metal oxide semiconductor (CMOS) compatible, including embedded floating gate and charge trapping EEPROM/Flash along with FeRAMS, FeFETs, MRAMs and ReRAMs.
- Provides a timely, highly practical look at state-of-the-art IoT memory implementations for an array of product applications
- Synthesizes basic science with original analysis of memory technologies for Internet of Things (IoT) based on the authors' extensive experience in the field
- Focuses on practical and timely applications throughout
- Features numerous illustrations, tables, application requirements, and photographs
- Considers memory related security issues in IoT devices
Memories for the Intelligent Internet of Things is a valuable working resource for electrical engineers and engineering managers working in the electronics system and semiconductor industries. It is also an indispensable reference/text for graduate and advanced undergraduate students interested in the latest developments in integrated circuit devices and systems.
Introduction to the Intelligent Internet of Things xi 1 Smart Cities as the Prototype of the Intelligent Internet of Things 1
1.1 Overview 1
1.2 Smart Cities 1
1.3 Smart Commerce as an Element of the Smart City 1
1.3.1 Smart Inventory Control 1
1.3.2 Smart Delivery 3
1.3.3 Smart Marketing Using Artificial Intelligence 3
1.4 Smart Residences 4
1.4.1 A City of Smart Connected Homes 4
1.5 People as Center of Smart Connected Homes 5
1.5.1 Wearable Electronics 5
1.5.2 Control Electronics 6
1.6 Smart Individual Transportation 6
1.6.1 Overview of Smart Automobiles 6
1.6.2 Driving Aids 7
1.6.3 Engine Processors 8
1.6.4 Auto Body Processors 8
1.6.5 Infotainment Processors 8
1.6.6 Autonomous Cars 8
1.7 Smart Transportation Networks 9
1.7.1 Smart Public Conveyance Networks 9
1.7.2 Individual Automotive Traffic Control 9
1.7.3 Smart Highways 10
1.8 Smart Energy Networks 10
1.8.1 Smart Electrical Meters 10
1.8.2 Smart Electrical Grids 12
1.9 Smart Connected Buildings 12
1.9.1 Smart Office Buildings 12
1.9.2 Smart Factories 13
1.9.3 Intelligent Hospitals 13
1.9.4 Smart Public Buildings 14
1.10 Thoughts 15 References 15 2 Memory Applications for the Intelligent Internet of Things 17
2.1 Introduction 17
2.2 Comparisons of the Various Nonvolatile Embedded Memories Characteristics 18
2.2.1 Embedded EEPROM, Flash, and Fuse Devices 18
2.2.2 Embedded Emerging Memory Devices in MCU 19
2.2.3 Required Properties of Embedded Nonvolatile Memories in Various Applications 21
2.3 Circuits Using Ultralow Power MCU with Embedded Memory for Energy Harvesting 23
2.3.1 Introduction to Ultralow Power MCU Using Energy Harvesting 23
2.3.2 Ultralow Power MCU with Embedded Flash Memory for Energy Harvesting 24
2.3.3 Ultralow Power MCU with Embedded FeRAM Memory for Energy Harvesting 24
2.3.4 Ultralow Power MCU with Embedded RRAM Memory for Energy Harvesting 26
2.3.5 Ultralow Power MCU for Energy Harvesting Power Management 26
2.4 Ultralow Power Battery Operated Flash MCU 27
2.4.1 Introduction to Ultralow Power Battery Operated Flash MCU 27
2.4.2 Ultralow Power Battery Operated Flash MCU with Embedded Flash Memory 28
2.4.3 Ultralow Power Battery Operated MCU with Embedded RRAM 29
2.4.4 Ultralow Power Battery Operated MCU with Embedded FeRAM 30
2.5 Nonvolatile MCUs Using Emerging Memory for Nonvolatile Logic 32
2.5.1 Nonvolatile Logic Arrays Using FeRAM 32
2.5.2 Nonvolatile Logic Arrays Using MTJ MRAM 35
2.5.3 Processors with RRAM for Nonvolatile Logic Arrays 37
2.6 Communication Protocols for Memory Sensor Tags 41
2.6.1 Radio Frequency Identification (RFID) Tags 41
2.6.2 Near Field Communications (NFC) 42
2.6.3 Bluetooth]Based Beacons and Sensor Nodes 43
2.6.4 IoT Devices with Wi]Fi 46
2.6.5 IoT Devices with USB Connectivity 47
2.6.6 Single Wire Connectivity 48
2.6.7 Zigbee Interface 48
2.6.8 ANT Interface 48
2.7 Wearable Medical Devices 49
2.7.1 Overview of Wearable Medical Devices 49
2.7.2 Miniature Hearing Aids Using FeRAM Memory 50
2.7.3 Body Sensor Node Platforms Using CB]RAM Memory 50
2.7.4 "Store Mostly" Healthcare Systems Using MRAM 50
2.7.5 Wearable Biomonitoring with NFC and eFeRAM Memory 51
2.7.6 Wearable Healthcare System with ECG Processor Using FeRAM 52
2.8 Low Power Battery Operated Medical Devices and Systems 55
2.8.1 Overview of Low Power Battery Operated Medical Devices 55
2.8.2 Low Power Battery Operated Medical Devices Using eFlash 55
2.8.3 LP Battery Operated Medical Devices Using Embedded Emerging Memories 59
2.8.4 Security for Medical Systems 60
2.9 Automotive Network Applications 61
2.9.1 Overview of the Automotive Application 61
2.9.2 Early Advanced Automotive Driver Assistance Systems 64
2.9.3 More Recent Advanced Driver Assistance Systems (ADAS) 65
2.9.4 Automotive Navigation and Positioning 66
2.9.5 Under]the]Hood Applications 66
2.9.6 MONOS Memory for Under]the]Hood Applications 68
2.9.7 Automotive Infotainment 69
2.9.8 Secure Automotive 70
2.9.9 Automotive Body Processors 70
2.10 Smart Electrical Grid and Digital Utility Smart Meters 71
2.10.1 Overview of the Smart Meter Market 71
2.10.2 Smart Meter Chips with Embedded Flash Memory 71
2.10.3 Smart Meter Chips with Large Embedded Flash Memory 71
2.11 Consumer Home Systems and Networks 74
2.11.1 Remote Controls 74
2.11.2 Environmental Sensors 75
2.11.3 Home Network Systems 75
2.12 Motor Control Chips with Embedded Memory 76
2.12.1 Small System Motor Control Using Embedded Memory 76
2.12.2 Motor Control for Multiple Motors Using Embedded MONOS Memory 76
2.12.3 Motor Control with Embedded NV FeRAM 77
2.13 Smart Chip Cards in Advanced Applications 77
2.14 Analysis of Big Data Server Memory Hierarchy for Storing IoT 78 References 80 3 Embedded Flash and EEPROM for Smart IoT 89
3.1 Introduction to eFlash and eEEPROM for Smart IoT 89
3.1.1 Overview of eFlash and eEEPROM for Smart IoT 89
3.1.2 Summary of Application Requirements for Embedded Flash in IoT 90
3.2 Single Poly Floating Gate eFlash/EEPROM Cells for IoT 91
3.2.1 Overview of Single Poly Floating Gate eFlash/EEPROM for IoT 91
3.2.2 Early Single Polysilicon Floating Gate EEPROMS 91
3.2.3 Single Poly EEPROM Cells for Specialty Applications 96
3.2.4 Multitime]Programmable Single Poly Embedded Nonvolatile eMemories 99
3.2.5 Recent Single Poly Fully CMOS Embedded EEPROM Devices 103
3.2.6 Single Polysilicon eNVM in High Voltage CMOS 106
3.3 eFlash Cells Using Multiple Single Polysilicon CMOS Logic Transistors 107
3.4 Split Gate Technology for Floating Gate Embedded Flash 112
3.4.1 Early Split Gate Embedded Flash Floating Gate Technology 112
3.4.2 Issues, Peripherals, and Applications]Specific FG Split Gate Memory 116
3.4.3 Advanced Split Gate Floating Gate Technology below 50 nm 124
3.5 Stacked Flash and Processor TSV Integration 127
3.6 OTP/ MTP Embedded Flash Cells and Fuses 127
3.7 Stacked Gate Double Poly Flash 130
3.8 Charge Trapping eFlash 133
3.8.1 Overview of Early Embedded Charge Trapping Memory 133
3.8.2 Embedded 40 nm Charge Trapping (MONOS) Flash MCU 136
3.8.3 Embedded 28 nm Charge Trapping (MONOS) Flash MCU 139
3.8.4 Embedded Application]Specific 1T]MONOS Flash Macro 141
3.8.5 FinFET SG]MONOS 142
3.8.6 Embedded Charge Trapping (SONOS) NOR Flash 144
3.8.7 Embedded 2T SONOS NVM in HV CMOS 147
3.8.8 Self]Aligned Nitride Logic NVM 148
3.8.9 p]Channel SONOS Embedded Flash 149
3.8.10 Charge Trap eFlash for Low Energy Applications 150
3.8.11 Blocking and Tunnel Oxide of DT BE]SONOS Performance 151
3.8.12 Novel Embedded Charge Trap Memories 152
3.9 Split Gate CT eFlash Nanocrystal Storage 158
3.10 Novel Embedded Flash Memory 160 References 161 4 Thin Film Polymer and Flexible Memories 169
4.1 Overview 169
4.2 Organic Ferroelectric Memories 169
4.2.1 Characteristics and Features of Organic Ferroelectric Memories 169
4.2.2 Printable Ferroelectric Embedded Memories 174
4.2.3 IoT Applications of Thin Film Ferroelectric Memory 179
4.3 Polymer Ferroelectric Tunnel Junctions 181
4.4 Types and Characteristics of Polymer Resistive RAMs with Flexible Substrate 181
4.4.1 Overview of Polymer Resistive RAMs with Flexible Substrate 181
4.4.2 Parylene]C]Based Resistive RAM 182
4.4.3 Cu Atom Switches 184
4.4.4 Inorganic Thin Film Resistive RAMs on Flexible Substrates 187
4.4.5 IZO and IGZO Resistive RAM Memories 189
4.4.6 Other Polymer Resistive RAMS with Flexible Substrates 192
4.5 Charge Trapping Nanoparticle (NP) Memory on Flexible Substrates 199
4.5.1 Overview of Charge Trapping NP Memory on Flexible Substrates 199
4.5.2 Carbon Nanotube Charge Trapping Memory with Flexible Substrates 200
4.5.3 Inkjet Printed Nanoparticle Memory 201
4.5.4 Other Nanoparticle Charge Trapping Memories on Flexible Substrates 202
4.6 Transfer of Conventional Memory Chips on to Flexible Substrates 206
4.6.1 Transfer of Silicon Chips Using SOI Base Wafers 206
4.6.2 Creating Thin Chips Using an Underlying Cavity 208
4.6.3 Fan]Out Wafer Level Packaging for Assembling Silicon Chips on Flexible Substrate 210 References 215 5 Neuromorphic Computing Using Emerging NV Memory Devices 221
5.1 Overviewof Resistive RAMs and Ferroelectric RAMs in Neuromorphic Systems 221
5.2 Various Resistive RAMs for use as Synapses in Neuromorphic Systems 221
5.2.1 Metal Oxide Resistive RAM (MO]RRAMs) as Synapses 221
5.2.2 Conductive Bridge RRAM (CB]RRAM) as Synapses 224
5.2.3 Phase Change Memory (PCM) as Synapses 225
5.2.4 PCMO RRAM as Synapses 226
5.2.5 RRAM with Simultaneous Potentiation and Depression 228
5.2.6 Other Nonvolatile Memories with Analog Properties 229
5.3 3D Neuromorphic Memories 230
5.3.1 Neuromorphic Architectures as Dense TSV 3D Structures 230
5.3.2 3D Vertical RRAMs as Synapses Connecting Neurons 231
5.4 Modeling and Characte
1.1 Overview 1
1.2 Smart Cities 1
1.3 Smart Commerce as an Element of the Smart City 1
1.3.1 Smart Inventory Control 1
1.3.2 Smart Delivery 3
1.3.3 Smart Marketing Using Artificial Intelligence 3
1.4 Smart Residences 4
1.4.1 A City of Smart Connected Homes 4
1.5 People as Center of Smart Connected Homes 5
1.5.1 Wearable Electronics 5
1.5.2 Control Electronics 6
1.6 Smart Individual Transportation 6
1.6.1 Overview of Smart Automobiles 6
1.6.2 Driving Aids 7
1.6.3 Engine Processors 8
1.6.4 Auto Body Processors 8
1.6.5 Infotainment Processors 8
1.6.6 Autonomous Cars 8
1.7 Smart Transportation Networks 9
1.7.1 Smart Public Conveyance Networks 9
1.7.2 Individual Automotive Traffic Control 9
1.7.3 Smart Highways 10
1.8 Smart Energy Networks 10
1.8.1 Smart Electrical Meters 10
1.8.2 Smart Electrical Grids 12
1.9 Smart Connected Buildings 12
1.9.1 Smart Office Buildings 12
1.9.2 Smart Factories 13
1.9.3 Intelligent Hospitals 13
1.9.4 Smart Public Buildings 14
1.10 Thoughts 15 References 15 2 Memory Applications for the Intelligent Internet of Things 17
2.1 Introduction 17
2.2 Comparisons of the Various Nonvolatile Embedded Memories Characteristics 18
2.2.1 Embedded EEPROM, Flash, and Fuse Devices 18
2.2.2 Embedded Emerging Memory Devices in MCU 19
2.2.3 Required Properties of Embedded Nonvolatile Memories in Various Applications 21
2.3 Circuits Using Ultralow Power MCU with Embedded Memory for Energy Harvesting 23
2.3.1 Introduction to Ultralow Power MCU Using Energy Harvesting 23
2.3.2 Ultralow Power MCU with Embedded Flash Memory for Energy Harvesting 24
2.3.3 Ultralow Power MCU with Embedded FeRAM Memory for Energy Harvesting 24
2.3.4 Ultralow Power MCU with Embedded RRAM Memory for Energy Harvesting 26
2.3.5 Ultralow Power MCU for Energy Harvesting Power Management 26
2.4 Ultralow Power Battery Operated Flash MCU 27
2.4.1 Introduction to Ultralow Power Battery Operated Flash MCU 27
2.4.2 Ultralow Power Battery Operated Flash MCU with Embedded Flash Memory 28
2.4.3 Ultralow Power Battery Operated MCU with Embedded RRAM 29
2.4.4 Ultralow Power Battery Operated MCU with Embedded FeRAM 30
2.5 Nonvolatile MCUs Using Emerging Memory for Nonvolatile Logic 32
2.5.1 Nonvolatile Logic Arrays Using FeRAM 32
2.5.2 Nonvolatile Logic Arrays Using MTJ MRAM 35
2.5.3 Processors with RRAM for Nonvolatile Logic Arrays 37
2.6 Communication Protocols for Memory Sensor Tags 41
2.6.1 Radio Frequency Identification (RFID) Tags 41
2.6.2 Near Field Communications (NFC) 42
2.6.3 Bluetooth]Based Beacons and Sensor Nodes 43
2.6.4 IoT Devices with Wi]Fi 46
2.6.5 IoT Devices with USB Connectivity 47
2.6.6 Single Wire Connectivity 48
2.6.7 Zigbee Interface 48
2.6.8 ANT Interface 48
2.7 Wearable Medical Devices 49
2.7.1 Overview of Wearable Medical Devices 49
2.7.2 Miniature Hearing Aids Using FeRAM Memory 50
2.7.3 Body Sensor Node Platforms Using CB]RAM Memory 50
2.7.4 "Store Mostly" Healthcare Systems Using MRAM 50
2.7.5 Wearable Biomonitoring with NFC and eFeRAM Memory 51
2.7.6 Wearable Healthcare System with ECG Processor Using FeRAM 52
2.8 Low Power Battery Operated Medical Devices and Systems 55
2.8.1 Overview of Low Power Battery Operated Medical Devices 55
2.8.2 Low Power Battery Operated Medical Devices Using eFlash 55
2.8.3 LP Battery Operated Medical Devices Using Embedded Emerging Memories 59
2.8.4 Security for Medical Systems 60
2.9 Automotive Network Applications 61
2.9.1 Overview of the Automotive Application 61
2.9.2 Early Advanced Automotive Driver Assistance Systems 64
2.9.3 More Recent Advanced Driver Assistance Systems (ADAS) 65
2.9.4 Automotive Navigation and Positioning 66
2.9.5 Under]the]Hood Applications 66
2.9.6 MONOS Memory for Under]the]Hood Applications 68
2.9.7 Automotive Infotainment 69
2.9.8 Secure Automotive 70
2.9.9 Automotive Body Processors 70
2.10 Smart Electrical Grid and Digital Utility Smart Meters 71
2.10.1 Overview of the Smart Meter Market 71
2.10.2 Smart Meter Chips with Embedded Flash Memory 71
2.10.3 Smart Meter Chips with Large Embedded Flash Memory 71
2.11 Consumer Home Systems and Networks 74
2.11.1 Remote Controls 74
2.11.2 Environmental Sensors 75
2.11.3 Home Network Systems 75
2.12 Motor Control Chips with Embedded Memory 76
2.12.1 Small System Motor Control Using Embedded Memory 76
2.12.2 Motor Control for Multiple Motors Using Embedded MONOS Memory 76
2.12.3 Motor Control with Embedded NV FeRAM 77
2.13 Smart Chip Cards in Advanced Applications 77
2.14 Analysis of Big Data Server Memory Hierarchy for Storing IoT 78 References 80 3 Embedded Flash and EEPROM for Smart IoT 89
3.1 Introduction to eFlash and eEEPROM for Smart IoT 89
3.1.1 Overview of eFlash and eEEPROM for Smart IoT 89
3.1.2 Summary of Application Requirements for Embedded Flash in IoT 90
3.2 Single Poly Floating Gate eFlash/EEPROM Cells for IoT 91
3.2.1 Overview of Single Poly Floating Gate eFlash/EEPROM for IoT 91
3.2.2 Early Single Polysilicon Floating Gate EEPROMS 91
3.2.3 Single Poly EEPROM Cells for Specialty Applications 96
3.2.4 Multitime]Programmable Single Poly Embedded Nonvolatile eMemories 99
3.2.5 Recent Single Poly Fully CMOS Embedded EEPROM Devices 103
3.2.6 Single Polysilicon eNVM in High Voltage CMOS 106
3.3 eFlash Cells Using Multiple Single Polysilicon CMOS Logic Transistors 107
3.4 Split Gate Technology for Floating Gate Embedded Flash 112
3.4.1 Early Split Gate Embedded Flash Floating Gate Technology 112
3.4.2 Issues, Peripherals, and Applications]Specific FG Split Gate Memory 116
3.4.3 Advanced Split Gate Floating Gate Technology below 50 nm 124
3.5 Stacked Flash and Processor TSV Integration 127
3.6 OTP/ MTP Embedded Flash Cells and Fuses 127
3.7 Stacked Gate Double Poly Flash 130
3.8 Charge Trapping eFlash 133
3.8.1 Overview of Early Embedded Charge Trapping Memory 133
3.8.2 Embedded 40 nm Charge Trapping (MONOS) Flash MCU 136
3.8.3 Embedded 28 nm Charge Trapping (MONOS) Flash MCU 139
3.8.4 Embedded Application]Specific 1T]MONOS Flash Macro 141
3.8.5 FinFET SG]MONOS 142
3.8.6 Embedded Charge Trapping (SONOS) NOR Flash 144
3.8.7 Embedded 2T SONOS NVM in HV CMOS 147
3.8.8 Self]Aligned Nitride Logic NVM 148
3.8.9 p]Channel SONOS Embedded Flash 149
3.8.10 Charge Trap eFlash for Low Energy Applications 150
3.8.11 Blocking and Tunnel Oxide of DT BE]SONOS Performance 151
3.8.12 Novel Embedded Charge Trap Memories 152
3.9 Split Gate CT eFlash Nanocrystal Storage 158
3.10 Novel Embedded Flash Memory 160 References 161 4 Thin Film Polymer and Flexible Memories 169
4.1 Overview 169
4.2 Organic Ferroelectric Memories 169
4.2.1 Characteristics and Features of Organic Ferroelectric Memories 169
4.2.2 Printable Ferroelectric Embedded Memories 174
4.2.3 IoT Applications of Thin Film Ferroelectric Memory 179
4.3 Polymer Ferroelectric Tunnel Junctions 181
4.4 Types and Characteristics of Polymer Resistive RAMs with Flexible Substrate 181
4.4.1 Overview of Polymer Resistive RAMs with Flexible Substrate 181
4.4.2 Parylene]C]Based Resistive RAM 182
4.4.3 Cu Atom Switches 184
4.4.4 Inorganic Thin Film Resistive RAMs on Flexible Substrates 187
4.4.5 IZO and IGZO Resistive RAM Memories 189
4.4.6 Other Polymer Resistive RAMS with Flexible Substrates 192
4.5 Charge Trapping Nanoparticle (NP) Memory on Flexible Substrates 199
4.5.1 Overview of Charge Trapping NP Memory on Flexible Substrates 199
4.5.2 Carbon Nanotube Charge Trapping Memory with Flexible Substrates 200
4.5.3 Inkjet Printed Nanoparticle Memory 201
4.5.4 Other Nanoparticle Charge Trapping Memories on Flexible Substrates 202
4.6 Transfer of Conventional Memory Chips on to Flexible Substrates 206
4.6.1 Transfer of Silicon Chips Using SOI Base Wafers 206
4.6.2 Creating Thin Chips Using an Underlying Cavity 208
4.6.3 Fan]Out Wafer Level Packaging for Assembling Silicon Chips on Flexible Substrate 210 References 215 5 Neuromorphic Computing Using Emerging NV Memory Devices 221
5.1 Overviewof Resistive RAMs and Ferroelectric RAMs in Neuromorphic Systems 221
5.2 Various Resistive RAMs for use as Synapses in Neuromorphic Systems 221
5.2.1 Metal Oxide Resistive RAM (MO]RRAMs) as Synapses 221
5.2.2 Conductive Bridge RRAM (CB]RRAM) as Synapses 224
5.2.3 Phase Change Memory (PCM) as Synapses 225
5.2.4 PCMO RRAM as Synapses 226
5.2.5 RRAM with Simultaneous Potentiation and Depression 228
5.2.6 Other Nonvolatile Memories with Analog Properties 229
5.3 3D Neuromorphic Memories 230
5.3.1 Neuromorphic Architectures as Dense TSV 3D Structures 230
5.3.2 3D Vertical RRAMs as Synapses Connecting Neurons 231
5.4 Modeling and Characte
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