New Frontiers in Automotive Electronics Packaging

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The automotive industry is undergoing a period of dramatic change in both its technology and its players. More than 80 companies are developing autonomous vehicles and many more are involved in providing sensors and computational systems for decision making for advanced driver assistance systems (ADAS). System design, package choices, materials, and process integration are critical to the successful implementation of the new safety features that are part of ADAS.

There is debate as to when autonomous-driving vehicles will be introduced on a wide scale, but there is no question that the use of safety features incorporating multiple sensors is increasing dramatically. ADAS adoption requires greater semiconductor content at each level of adoption. It requires the use of cameras, light detection and ranging (LIDAR), radar, and other sensors, as well as communication systems and fast processing capability. The potential use of artificial intelligence or machine learning to provide the analytics enabling safety features is driving the adoption of advanced packaging and heterogeneous integration.

The focus of the market forecast in this report is for new packages introduced as a result of ADAS adoption. Different combinations of sensors will be used in each vehicle and the particular combinations will be determined by each carmaker. For example, the Audi A8 uses one camera, five radars, two ultrasonic sensors, and one LIDAR system. Tesla’s autonomous driving system uses four to eight cameras, 12 ultrasonic sensors, and one forward-facing radar.

This report also examines the impact of the adoption of electric vehicles on packages used for the powertrain.
  • Contents…
    • Executive Summary
      • ​Image Sensors
      • Radar
      • LIDAR
      • Sensor Fusion
      • Powertrain
      • Reliability Requirements
    • 1 Introduction
    • 2 Advanced Driver Assist Systems
      • 2.1 ADAS Sensors
        • 2.1.1 Image Sensors
        • 2.1.2 Radar-based Sensors
        • 2.1.3 LIDAR
        • 2.1.4 Ultrasonic Sensors
      • 2.2 Vehicle to X Communication (V2X, V2V, V2I)
      • 2.3 Heads Up Display
      • 2.4 Sensor Fusion
    • 3 Infotainment and Security
      • 3.1 Infotainment
      • 3.2 Accident Reporting and Monitoring
      • 3.3 Smart Key
    • ​4 Boards, Components, Interconnect
      • 4.1 PCB Considerations for RF
      • 4.2 Passive Components
        • 4.2.1 Capacitors
        • 4.2.2 Resistors
      • 4.3 Interconnect Trends
        • 4.3.1 Wire Bonding
        • 4.3.2 Cu Clip
        • 4.3.3 Flip Chip
      • 4.4 IC Package Substrate Suppliers
      • 4.5 OSATs with Automotive Assembly
    • 5 Passive Safety Systems
      • 5.1 Accelerometers
      • 5.2 Tire Pressure Management Systems
      • 5.3 Air Bag Sensors
    • 6 Powertrain
      • 6.1 Electric Vehicle Powertrain Packaging
        • 6.1.1 Hybrid Electric Vehicles
        • 6.1.2 Electric Vehicles
        • 6.1.3 Automotive MOSFETs
      • 6.2 Key Advanced Packaging Developments
        • 6.2.1 Wide Band Gap Devices
        • 6.2.2 Double-sided Cooling
        • 6.2.3 Embedded Die Power Packaging
        • 6.2.4 Mechatronic Integration
    • 7 Reliability Requirements
      • 7.1 Standards Based Qualification
      • 7.2 Knowledge Based Qualification
      • 7.3 Design for Reliability Method
      • 7.4 Impact of ISO 26262 ASIL Analysis
    • 8 ADAS Package Market Forecast
      • 8.1 Cameras
      • 8.2 Radar
      • 8.3 LIDAR
      • 8.4 Sensor Fusion and Infotainment
      • 8.5 EV Powertrain Packages
    • Appendix (glossary)
    • References
  • Figures…
    • OV9716 image sensor.
    • Panasonic organic CMOS image sensor structure.
    • Calterah Semiconductor mmWave radar in FO-WLPs.
    • Stacked LIDAR photo detector.
    • LeddarVu Vu8.
    • Driver assistance sensors on Tesla Model X.
    • Laser beam scanning bi-axial MEMS mirror platform.
    • Map of low dielectric constant RF materials.
    • FET package with Cu clip.
    • Powertrain components in hybrid electric vehicle.
    • Powertrain components in an electric vehicle.
    • HybridPACK™ Drive IGBT module for inverter applications.
    • HEV/EV inverter trends.
    • Performance of Si vs. WBG semiconductor for automotive power.
    • Standard and double-sided cooling (DSC) package structures.
    • Reduction of resistive and inductive loss in an embedded
    • Physics of failure approach for an electronic component.
    • Quality and reliability in 125°C mission profile component.
  • Tables…
    • CMOS Image Sensor Market Projection for ADAS
    • FO-WLP Market Projection for Radar Packages
    • Sensor Fusion Processor Market Projection
    • EV Package Market Projection Based on Chevy Bolt
    • Market Projection for MOSFETs in EVs
    •  Automotive Revenue for Selected Companies
    • ADAS Features with Sensors
    • Sensors for ADAS
    • Suppliers of Sensors or Systems Using Sensors
    • Key Specifications of Sony Image Sensor
    • Packages for mmWave Radar
    • Radar Sensor Packages
    • Highly Automated Driving Conditions
    • Innoluce 1D MEMS Mirror Products
    • DSRC is Complementary to Cellular V2X
    • Selected ADAS Sensor Fusion Packages
    • NVIDIA’s DRIVE PX Products
    • Infotainment System Suppliers
    • AEC Q200 Passive Component Grades Based on Use
    • Cu Wire Reliability Test Results
    • OSATs with Cu Wire Bond for Automotive
    • Cu Wire Bond for Automotive Controller Packages
    • Selected Substrate Suppliers for Automotive
    • OSATs with Automotive Assembly
    • Powertrain High-Power Device Examples
    • Powertrain Device and Module Requirements
    • Development of Si-IGBT for Prius
    • Powertrain Inverter and DC/DC Converter Manufacturers
    • Performance Requirements Driving Powertrain Packaging Technology
    • Electrical Properties of Si and WBG Materials
    • Advantages of WBG Devices in Powertrain Power Modules
    • Increasing Output Power of a Powertrain Inverter
    • Comparison of Double-Sided and Single-Sided Cooling
    • Mechatronically Integrated Motor Control Circuit
    • Roadmap for Power Modules and Motor Control
    • AEC-Q100 Grades Based on the Operating Environment
    • AEC-Q100 Qualification Tests
    • Use Time by Vehicle Type
    • Operating Situations for E-Vehicles
    • Automotive vs. Consumer Potential Differences
    • CMOS Image Sensor Market Projection for ADAS
    • FO-WLP Market Projection for Radar Packages
    • Sensor Fusion Processor Market Projection
    • EV Package Market Projection Based on Chevy Bolt
    • Market Projection for MOSFETs in EVs
Stacks Image 26136
  • Published January 2018
  • 116 pages
  • 18 figures / 45 tables
  • 94 PowerPoint slides
  • $5,100 single user
    $8,750 corporate license
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