Memories for the Intelligent Internet of Things

BETTY PRINCE, PHD has over thirty years of experience in the semiconductor industry, having worked with Texas Instruments, N.V. Philips, Motorola, R.C.A., and Fairchild. She is currently CEO of Memory Strategies International, Leander, Texas, USA. She holds patents in the memory, processor and interface designs. DAVID PRINCE has worked with the memory reports written by Memory Strategies International for the last eighteen years. He holds degrees in Computer Science, Physics, and Astronomy from the University of Texas.

• Introduction to the Intelligent Internet of Things xi1 Smart Cities as the Prototype of the Intelligent Internet of Things 11.1 Overview 11.2 Smart Cities 11.3 Smart Commerce as an Element of the Smart City 11.3.1 Smart Inventory Control 11.3.2 Smart Delivery 31.3.3 Smart Marketing Using Artificial Intelligence 31.4 Smart Residences 41.4.1 A City of Smart Connected Homes 41.5 People as Center of Smart Connected Homes 51.5.1 Wearable Electronics 51.5.2 Control Electronics 61.6 Smart Individual Transportation 61.6.1 Overview of Smart Automobiles 61.6.2 Driving Aids 71.6.3 Engine Processors 81.6.4 Auto Body Processors 81.6.5 Infotainment Processors 81.6.6 Autonomous Cars 81.7 Smart Transportation Networks 91.7.1 Smart Public Conveyance Networks 91.7.2 Individual Automotive Traffic Control 91.7.3 Smart Highways 101.8 Smart Energy Networks 101.8.1 Smart Electrical Meters 101.8.2 Smart Electrical Grids 121.9 Smart Connected Buildings 121.9.1 Smart Office Buildings 121.9.2 Smart Factories 131.9.3 Intelligent Hospitals 131.9.4 Smart Public Buildings 141.10 Thoughts 15References 152 Memory Applications for the Intelligent Internet of Things 172.1 Introduction 172.2 Comparisons of the Various Nonvolatile Embedded Memories Characteristics 182.2.1 Embedded EEPROM, Flash, and Fuse Devices 182.2.2 Embedded Emerging Memory Devices in MCU 192.2.3 Required Properties of Embedded Nonvolatile Memories in Various Applications 212.3 Circuits Using Ultralow Power MCU with Embedded Memory for Energy Harvesting 232.3.1 Introduction to Ultralow Power MCU Using Energy Harvesting 232.3.2 Ultralow Power MCU with Embedded Flash Memory for Energy Harvesting 242.3.3 Ultralow Power MCU with Embedded FeRAM Memory for Energy Harvesting 242.3.4 Ultralow Power MCU with Embedded RRAM Memory for Energy Harvesting 262.3.5 Ultralow Power MCU for Energy Harvesting Power Management 262.4 Ultralow Power Battery Operated Flash MCU 272.4.1 Introduction to Ultralow Power Battery Operated Flash MCU 272.4.2 Ultralow Power Battery Operated Flash MCU with Embedded Flash Memory 282.4.3 Ultralow Power Battery Operated MCU with Embedded RRAM 292.4.4 Ultralow Power Battery Operated MCU with Embedded FeRAM 302.5 Nonvolatile MCUs Using Emerging Memory for Nonvolatile Logic 322.5.1 Nonvolatile Logic Arrays Using FeRAM 322.5.2 Nonvolatile Logic Arrays Using MTJ MRAM 352.5.3 Processors with RRAM for Nonvolatile Logic Arrays 372.6 Communication Protocols for Memory Sensor Tags 412.6.1 Radio Frequency Identification (RFID) Tags 412.6.2 Near Field Communications (NFC) 422.6.3 Bluetooth]Based Beacons and Sensor Nodes 432.6.4 IoT Devices with Wi]Fi 462.6.5 IoT Devices with USB Connectivity 472.6.6 Single Wire Connectivity 482.6.7 Zigbee Interface 482.6.8 ANT Interface 482.7 Wearable Medical Devices 492.7.1 Overview of Wearable Medical Devices 492.7.2 Miniature Hearing Aids Using FeRAM Memory 502.7.3 Body Sensor Node Platforms Using CB]RAM Memory 502.7.4 “Store Mostly” Healthcare Systems Using MRAM 502.7.5 Wearable Biomonitoring with NFC and eFeRAM Memory 512.7.6 Wearable Healthcare System with ECG Processor Using FeRAM 522.8 Low Power Battery Operated Medical Devices and Systems 552.8.1 Overview of Low Power Battery Operated Medical Devices 552.8.2 Low Power Battery Operated Medical Devices Using eFlash 552.8.3 LP Battery Operated Medical Devices Using Embedded Emerging Memories 592.8.4 Security for Medical Systems 602.9 Automotive Network Applications 612.9.1 Overview of the Automotive Application 612.9.2 Early Advanced Automotive Driver Assistance Systems 642.9.3 More Recent Advanced Driver Assistance Systems (ADAS) 652.9.4 Automotive Navigation and Positioning 662.9.5 Under]the]Hood Applications 662.9.6 MONOS Memory for Under]the]Hood Applications 682.9.7 Automotive Infotainment 692.9.8 Secure Automotive 702.9.9 Automotive Body Processors 702.10 Smart Electrical Grid and Digital Utility Smart Meters 712.10.1 Overview of the Smart Meter Market 712.10.2 Smart Meter Chips with Embedded Flash Memory 712.10.3 Smart Meter Chips with Large Embedded Flash Memory 712.11 Consumer Home Systems and Networks 742.11.1 Remote Controls 742.11.2 Environmental Sensors 752.11.3 Home Network Systems 752.12 Motor Control Chips with Embedded Memory 762.12.1 Small System Motor Control Using Embedded Memory 762.12.2 Motor Control for Multiple Motors Using Embedded MONOS Memory 762.12.3 Motor Control with Embedded NV FeRAM 772.13 Smart Chip Cards in Advanced Applications 772.14 Analysis of Big Data Server Memory Hierarchy for Storing IoT 78References 803 Embedded Flash and EEPROM for Smart IoT 893.1 Introduction to eFlash and eEEPROM for Smart IoT 893.1.1 Overview of eFlash and eEEPROM for Smart IoT 893.1.2 Summary of Application Requirements for Embedded Flash in IoT 903.2 Single Poly Floating Gate eFlash/EEPROM Cells for IoT 913.2.1 Overview of Single Poly Floating Gate eFlash/EEPROM for IoT 913.2.2 Early Single Polysilicon Floating Gate EEPROMS 913.2.3 Single Poly EEPROM Cells for Specialty Applications 963.2.4 Multitime]Programmable Single Poly Embedded Nonvolatile eMemories 993.2.5 Recent Single Poly Fully CMOS Embedded EEPROM Devices 1033.2.6 Single Polysilicon eNVM in High Voltage CMOS 1063.3 eFlash Cells Using Multiple Single Polysilicon CMOS Logic Transistors 1073.4 Split Gate Technology for Floating Gate Embedded Flash 1123.4.1 Early Split Gate Embedded Flash Floating Gate Technology 1123.4.2 Issues, Peripherals, and Applications]Specific FG Split Gate Memory 1163.4.3 Advanced Split Gate Floating Gate Technology below 50 nm 1243.5 Stacked Flash and Processor TSV Integration 1273.6 OTP/ MTP Embedded Flash Cells and Fuses 1273.7 Stacked Gate Double Poly Flash 1303.8 Charge Trapping eFlash 1333.8.1 Overview of Early Embedded Charge Trapping Memory 1333.8.2 Embedded 40 nm Charge Trapping (MONOS) Flash MCU 1363.8.3 Embedded 28 nm Charge Trapping (MONOS) Flash MCU 1393.8.4 Embedded Application]Specific 1T]MONOS Flash Macro 1413.8.5 FinFET SG]MONOS 1423.8.6 Embedded Charge Trapping (SONOS) NOR Flash 1443.8.7 Embedded 2T SONOS NVM in HV CMOS 1473.8.8 Self]Aligned Nitride Logic NVM 1483.8.9 p]Channel SONOS Embedded Flash 1493.8.10 Charge Trap eFlash for Low Energy Applications 1503.8.11 Blocking and Tunnel Oxide of DT BE]SONOS Performance 1513.8.12 Novel Embedded Charge Trap Memories 1523.9 Split Gate CT eFlash Nanocrystal Storage 1583.10 Novel Embedded Flash Memory 160References 1614 Thin Film Polymer and Flexible Memories 1694.1 Overview 1694.2 Organic Ferroelectric Memories 1694.2.1 Characteristics and Features of Organic Ferroelectric Memories 1694.2.2 Printable Ferroelectric Embedded Memories 1744.2.3 IoT Applications of Thin Film Ferroelectric Memory 1794.3 Polymer Ferroelectric Tunnel Junctions 1814.4 Types and Characteristics of Polymer Resistive RAMs with Flexible Substrate 1814.4.1 Overview of Polymer Resistive RAMs with Flexible Substrate 1814.4.2 Parylene]C]Based Resistive RAM 1824.4.3 Cu Atom Switches 1844.4.4 Inorganic Thin Film Resistive RAMs on Flexible Substrates 1874.4.5 IZO and IGZO Resistive RAM Memories 1894.4.6 Other Polymer Resistive RAMS with Flexible Substrates 1924.5 Charge Trapping Nanoparticle (NP) Memory on Flexible Substrates 1994.5.1 Overview of Charge Trapping NP Memory on Flexible Substrates 1994.5.2 Carbon Nanotube Charge Trapping Memory with Flexible Substrates 2004.5.3 Inkjet Printed Nanoparticle Memory 2014.5.4 Other Nanoparticle Charge Trapping Memories on Flexible Substrates 2024.6 Transfer of Conventional Memory Chips on to Flexible Substrates 2064.6.1 Transfer of Silicon Chips Using SOI Base Wafers 2064.6.2 Creating Thin Chips Using an Underlying Cavity 2084.6.3 Fan]Out Wafer Level Packaging for Assembling Silicon Chips on Flexible Substrate 210References 2155 Neuromorphic Computing Using Emerging NV Memory Devices 2215.1 Overviewof Resistive RAMs and Ferroelectric RAMs in Neuromorphic Systems 2215.2 Various Resistive RAMs for use as Synapses in Neuromorphic Systems 2215.2.1 Metal Oxide Resistive RAM (MO]RRAMs) as Synapses 2215.2.2 Conductive Bridge RRAM (CB]RRAM) as Synapses 2245.2.3 Phase Change Memory (PCM) as Synapses 2255.2.4 PCMO RRAM as Synapses 2265.2.5 RRAM with Simultaneous Potentiation and Depression 2285.2.6 Other Nonvolatile Memories with Analog Properties 2295.3 3D Neuromorphic Memories 2305.3.1 Neuromorphic Architectures as Dense TSV 3D Structures 2305.3.2 3D Vertical RRAMs as Synapses Connecting Neurons 2315.4 Modeling and Characterization of RRAMs as Synaptic Devices 2365.5 Spiking Neural Nets, STDP, Potentiation, and Depression 2395.5.1 Introduction to Spiking Neural Networks 2395.5.2 Hybrid RRAM/CMOS STDP Neuromorphic Systems 2395.5.3 Memory Synapse and Neuron Systems 2445.5.4 Novel RRAM Synapse Applications 2475.6 Neural Network Systems Using Ferroelectric RAM Technology 2505.6.1 Neural Network Circuits Using Ferroelectric Memory (FeMEM) Synapses 2505.6.2 Using the FeMEM in Neural Network Circuits 2515.6.3 Ferroelectric Tunnel Junctions in Neuromorphic Circuits 2525.7 Early Neuromorphic Computers Using Phase Change Memory 2545.8 Resistive RAMs in Neuromorphic System Design and Application 2575.8.1 Design for Synaptic Devices for Neuromorphic Computing 2575.8.2 Using RRAMs in Various Neuromorphic Computing Applications 2595.8.3 Large RRAM Array Design for Neuromorphic Computing 2605.8.4 Advantages of RRAM over SRAM Crossbar Arrays in Matrix Multiplication 2625.9 Neuromorphic Memories Using Polymer and Flexible Memories 262References 2666 Big Data Search Engines and Deep Computers 2716.1 Overview of Big Data Search Engines and Deep Computers 2716.2 Content Addressable Memories Made Using Various Emerging Nonvolatile Memories 2716.2.1 Ternary CAMs Using Resistive RAMS 2726.2.2 CAMs Made Using Magnetic Memory 2736.2.3 CAMs Using Other Emerging Memories 2766.3 Components of Large Search Engines and ArtificialNeural Networks 2766.3.1 Using RRAMs in Look]Up Tables in Large Search Engines 2766.3.2 Using STT MRAM in Large Artificial Neural Networks 2786.4 Memory Issues in Deep Learning Systems 2816.4.1 Issues with Partitioning SRAM and RRAM Synaptic Arrays 2816.4.2 Issues of RRAM Variability for Extreme Learning Machine Architectures 2836.4.3 Issues with RRAM Memories in Restricted Boltzman Machines 2846.4.4 Large Neural Networks Using Memory Synapses 2876.5 Deep Neural Nets for IoT 2896.5.1 Types of Deep Neural Nets for IoT 2896.5.2 Deep Neural Nets for Noisy Data 2916.5.3 Deep Neural Nets for Speech and Vision Recognition 2936.5.4 Deep Neural Nets for Other Applications 298References 2997 Memory in Security Issues for IoT 3037.1 Introduction to Memory in Security Issues for IoT 3037.2 Memories Used as Physical Unclonable Functions (PUFs) 3037.2.1 Using RRAM for a Physical Unclonable Function 3047.2.2 Using MRAMs as Physical Unclonable Functions 3117.2.3 Using Flash Memory as a Physical Unclonable Function 3157.2.4 Other Memories used as Physical Unclonable Functions 3167.3 On]Chip Memory]Based Security Systems 3167.3.1 Introduction to On]Chip Security Systems 3167.3.2 Physically Secure Key and TAG Storage 3167.3.3 Face and Feature Detection in Security Systems 3197.3.4 Security in Embedded Systems 320References 321Index 323

Overall, this book is very informative and useful for engineers and scientist working in the semiconductor industry or, more specifically, on memory technologies. It gives a state-of-the-art overview on memories in IoT applications. Moreover, it provides a good introduction into the IoT applications and the demands for memories in IoT. - Stephan Menzel, Peter Grünberg Institute, Germany In their new book, Memories for the Intelligent Internet of Things, Betty Prince, with her co-author David Prince, once again deliver to her high standards of content: the book provides the essential details from a whopping 456 technical articles and refereed papers on every memory technology imaginable, from common flash memory to roll-printed organic polymers�This book will prove useful to anyone who needs to rapidly gain a broad understanding of the Intelligent IoT and all of the memory types that it can potentially use. - Jim Handy, OBJECTIVE ANALYSIS: Semiconductor Market Research, USA Industrial maturity usually goes hand in hand with extreme specialization. The IoT driven renaissance in new memory technologies however calls out for a treatment that is both broad and deep. Luckily two of the few polymaths in the area have done it proud. �Memories for the Intelligent Internet of Things� by Betty and David Prince is the best so far. With almost five hundred references and an excellent first chapter that explains the Intelligent IoT using �Smart Cities� to set the scene, all of the incumbent and new solid-state memory technologies are covered: Flash, EEPROM, FeRAM, RRAM, MRAM, PCM, SONOS, MONOS, CB-RAM. It�s all here. From device cross sections all the way to system implementation. I heartily recommend this book to anyone interested in this area, from academics and engineers working in the area to those curious about solid-state memories and what is driving their development. The shaking memory hierarchy has been explained. - Andrew Walker - CEO/Founder of Schiltron Corporation; VP of Spin Memory