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1. | INTRODUCTION |
1.1. | Towards more comfortable, safer and more autonomous vehicles |
1.2. | Towards ADAS and Autonomous Driving: increasing sensor content |
1.3. | Towards ADAS and Autonomous Driving: increasing radar use |
1.4. | SRR, MRR and LRR: Different functions |
1.5. | The evolving role of the automotive radar towards full 360deg 4D imaging radar |
1.6. | Automotive radars: role of legislation in driving the market |
1.7. | Automotive radars: frequency trends |
1.8. | Radar: which parameters limit the achievable KPIs |
1.9. | Impact of frequency and bandwidth on angular resolution |
1.10. | Why are radars essential to ADAS and autonomy? |
1.11. | What is sensor fusion? |
1.12. | Towards autonomy: Increasing semiconductor use |
1.13. | Performance levels of existing automotive radars |
1.14. | Radar players and market share |
1.15. | Radar market forecasts (2020-2040) in all levels of autonomy/ADAS in vehicles and trucks (unit numbers) |
1.16. | Radar market forecasts (2020-2040) in all levels of autonomy/ADAS in vehicles and trucks (market value) |
1.17. | Radar semiconductor market share forecast (GaAs, SiGe, Si) |
2. | SEMICONDUCTOR TRENDS |
2.1. | Ten year (unit number) market forecasts for automotive radars |
2.2. | Benchmarking of semiconductor technologies for mmwave radars |
2.3. | The choice of the semiconductor technology |
2.4. | SiGe: current and emerging performance levels |
2.5. | SiGe: overview and comparison of manufacturers |
2.6. | SiGe BiCMOS: Infineon Technology |
2.7. | SiGe BiCMOS: NXP (Freescale) Technology |
2.8. | SiGe BiCMOS: ST Microelectronics |
2.9. | A closer look at SiGe vs Si CMOS |
2.10. | Emerging all Si CMOS radar IC packages: NXP |
2.11. | Emerging all Si CMOS radar IC packages: ADI |
2.12. | Emerging all Si CMOS radar IC packages: TI |
2.13. | Many chip makers are on-board |
3. | PACKAGING TRENDS |
3.1. | Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond? |
3.2. | Packaging trends: AiP goes commercial? |
3.3. | Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond? |
3.4. | Comparison of die vs packaged options |
3.5. | eWLP vs flip chip and BGA in terms of insertion loss |
3.6. | Radar packaging: Material opportunities |
3.7. | Glass and panel level packaging of radars? |
3.8. | Function integration trend: from discreet to full chip-level function integration |
3.9. | Function integration trends: towards true radar-in-a-chip |
3.10. | Evolution of radar chips towards all-in-one designs |
4. | BOARD-LEVEL TRENDS AND OPPORTUNITIES |
4.1. | Board trends: from separate RF board to hybrid to full package integration? |
4.2. | Hybrid board is the norm |
4.3. | Hybrid board: what is it |
4.4. | Packaging trends: AiP goes commercial? |
5. | MATERIAL REQUIREMENTS FOR LOW INSERTION LOSS |
5.1. | Overview of the high level requirements for high frequency operation |
5.2. | Interconnect design for high frequency electronics |
5.3. | Passives: scaling challenges with frequency |
5.4. | Passives: transition towards embedded |
5.5. | Effect of low dielectric constant (I): feature sizes |
5.6. | Effect of low dielectric constant (II): thinness |
5.7. | Thinning the substrate at high frequencies: the challenge |
5.8. | Dielectric constant: benchmarking different substrate technologies |
5.9. | Dielectric constant: stability vs frequency for different organic substrates (PI, PTFE, LCP, thermosets, etc.) |
5.10. | Dielectric constant: stability vs frequency for different inorganic substrates (LTCC, glass) |
5.11. | Loss tangent: benchmarking different substrate technologies |
5.12. | Loss tangent: stability vs frequency for different substrates |
5.13. | Dielectric constant and loss tangent stability: behaviour at mmwave frequencies and higher |
5.14. | Temperature stability of dielectric constant: benchmarking organic substrates |
5.15. | Temperature stability of dielectric parameters of HTCC and LTCC alumina |
5.16. | Moisture uptake: benchmarking different substrate technologies |
5.17. | AlN vs other HTCC and LTCC materials |
6. | OTHER TRENDS |
6.1. | Key trends: from semiconductor to packaging to board technologies |
6.2. | Other trends: moving beyond just object detection |
6.3. | Other trends: increasing range, angular and elevation resolution |
6.4. | Towards large radar MIMO |
6.5. | Other trends: blurring the boundary between radar and lidar |
7. | SIGNAL PROCESSING |
7.1. | From assisted driving radar to radars for highly-autonomous driving |
7.2. | Operational mechanism and data sets of a FMCW radar |
7.3. | Signal processing path from front-end to data-output with range, velocity, presence, etc. map |
7.4. | FFTs to extract range, velocity, and object presence maps |
7.5. | Radar: how to measure the angle |
7.6. | Self-localization using radars |
7.7. | Map localization in maps using radar for path planning |
7.8. | Terminologies explained: AI, machine learning, artificial neural networks, deep neural networks |
7.9. | Radar data and processing: will it impact the value chain? |
7.10. | What we mean by object detection, identification, and tracking? |
7.11. | Radar : identification challenge |
7.12. | Artificial intelligence: waves of development |
7.13. | Classical method: feature descriptors |
7.14. | Typical image detection deep neutral network |
7.15. | Algorithm training process in a single layer |
7.16. | Towards deep learning by deepening the neutral network |
7.17. | The main varieties of deep learning approaches explained |
7.18. | How good is 2D object detection today? |
7.19. | What is the status of 3D object detection? Why it lags behind? |
7.20. | Towards 3D object detection: fused 2.5D networks |
7.21. | Deep neutral networks for radar data processing |
7.22. | Radars: the trade-off between speed and pre-processing of data (vs raw data) |
7.23. | Radars: the choice of system architecture and the choice of pre-processing of data |
7.24. | Data transfer speed: pre- or post-process radar data? |
7.25. | Radar data: challenges of spare point cloud |
7.26. | Data fusion challenge: mismatch in point cloud densities |
7.27. | Training neutral networks on radar data: the labelling challenge |
7.28. | Automatic data labelling: early fusion of camera, lidar and radar data |
7.29. | Developing ground truth and training data for data fusion and deep learning (including radar data) |
7.30. | Astyx Dataset HiRes2019 |
7.31. | Resolving the positional uncertainty in reference camera images |
7.32. | Radar object classification: using target-level data only |
7.33. | Radar object classification: using target-level data only and doing classification on clusters |
7.34. | Radar object classification: combining raw radar cube data with target-level data to improve performance |
8. | INTERFERENCE MITIGATION |
8.1. | Interference challenge |
8.2. | Known mitigation approaches for radar interference |
9. | PROMISING START-UPS |
9.1. | Uhnder: digital automotive radar-on-a-chip |
9.2. | Arbe |
9.2.1. | Arbe Robotics: high-performance radar with trained deep neutral networks |
9.2.2. | Arbe Robotics: high-performance 4D radar imaging |
9.3. | Metawave |
9.3.1. | Metawave: mmwave electronically steerable radar |
9.3.2. | Metawave: the hybrid beam forming architecture |
9.3.3. | Metawave: the hybrid beam forming architecture |
9.3.4. | Metawave: high interference mitigation capabilities |
9.3.5. | Metawave: high angular resolution at long range with electronically scanned high-frequency radar beams |
9.3.6. | Metawave: deep learning and fusion with other sensor data |
9.4. | Imec |
9.4.1. | Imec: In-cabin monitoring and gesture recognition using 145GHz radar |
9.5. | Steradian Semi: start-up developing 4D radar |
9.6. | Kymeta: metamaterials satellite antenna |
9.7. | Echodyne: Metamaterial Electronically Scanning Array |
9.8. | Metawave: using metal material to do beam forming with low side lobes |
9.9. | Zendar: high-res imaging radar for automotive |
9.10. | Vayyar: massive MIMO single-chip UWB radar solution |
9.11. | Neteera: 122GHz Si-based antenna-integrated single-package solution |
9.12. | Novelda AS: lower-power UWB radar for occupancy and respiration sensing |
9.13. | Oculli: towards 4D imaging radar |
9.14. | Omniradar (Staal Technologies): single chip 60GHz radar |
9.15. | Lunewave: 360deg azimuth view using 3D printed Lineburg lens |
9.16. | General Radar Corp: short to long range 3D scanning radar for the 76~81GHz automotive radars |
9.17. | Silicon Radar GmbH: radar chip design on SiGe BiCMOS |
9.18. | GhosWave: minimising mutul radar interference |
9.19. | Smartmicro GmbH |
9.20. | InnoSent GmbH |
10. | MARKET ANALYSIS |
10.1. | ADAS (level 1 and 2) |
10.1.1. | Towards more comfortable, safer and more autonomous vehicles |
10.1.2. | Towards ADAS and Autonomous Driving: increasing radar use |
10.1.3. | Regulation pushing adoption of ADAS 1 and 2 |
10.1.4. | Market forecasts (2020 to 2040) for ADAS and autonomous driving (level 3, 4, and 5) in passenger vehicles and robotaxis |
10.1.5. | Radar forecast (2020-2040) in ADAS level 1 and 2 |
10.2. | Autonomous private passenger cars and robotaxis (levels 3, 4, and 5) |
10.2.1. | Why autonomous cars |
10.2.2. | Challenges to traditional OEMs |
10.2.3. | Future mobility scenarios: autonomous and shared |
10.2.4. | Product and value positioning of autonomous cars |
10.2.5. | OEMs are becoming mobility service providers |
10.2.6. | What are the levels of automation in cars |
10.2.7. | The automation levels in details |
10.2.8. | Functions of autonomous driving at different levels |
10.2.9. | Roadmap of autonomous driving functions |
10.2.10. | Two development paths towards autonomous driving |
10.2.11. | Autonomous driving is changing the automotive supply chain |
10.2.12. | Auto OEMs' partnerships in autonomous driving |
10.2.13. | Overview of autonomous car launch time by OEMs |
10.2.14. | AV testing distance in California by companies |
10.2.15. | Waymo leading the game in terms of disengagement rate |
10.2.16. | AV testing by auto OEMs in 2018 |
10.2.17. | Autonomous driving test in Beijing, China |
10.2.18. | Autonomous driving in China: from testing to pilot services |
10.2.19. | OEMs are becoming mobility service providers |
10.2.20. | Mobility services launched by auto OEMs |
10.2.21. | Mobility service cost: autonomous vs non-autonomous |
10.2.22. | Overview of robotaxi launch time announced by AV companies |
10.2.23. | Travel demand and mobility as a service (MaaS) |
10.2.24. | Passenger car sales will peak earlier than expected |
10.2.25. | Passenger car sales forecast 2020-2040 - moderate |
10.2.26. | Global autonomous passenger car sales forecast 2020-2040 |
10.2.27. | Radar market forecasts (2020-2040) in all levels of autonomy/ADAS in vehicles and trucks (unit numbers) |
11. | AUTONOMOUS TRUCKS |
11.1. | Pain points in the trucking industry |
11.2. | Why autonomous trucks? |
11.3. | Automation levels of trucking explained |
11.4. | Funding race for autonomous truck start-ups |
11.5. | Announced deployment of L4+ autonomous trucks |
11.6. | Major stakeholders in autonomous trucking |
11.7. | Market readiness level of L4+ autonomous truck companies |
11.8. | Evolving autonomous applications for trucks |
11.9. | What is truck platooning? |
11.10. | Market share forecast for autonomous trucks 2020-2040 |
11.11. | Radar market forecasts (2020-2040) in all levels of autonomy/ADAS in vehicles and trucks (unit numbers) |
12. | MM-WAVE 5G: BEAM FORMING TECHNOLOGIES, ARCHITECTURES, AND ICS |
12.1. | Motivation of 5G: increasing the bandwidth |
12.2. | 5G station installation forecast by frequency |
12.3. | Shift to higher frequencies shrinks the antenna |
12.4. | Solving the high power loss at high frequency challenge: High antenna gain increases distance |
12.5. | Solving the high power loss at high frequency challenge: High antenna gain increases distance |
12.6. | Choice of semiconductor at high frequencies |
12.7. | Antenna array: can we do it with silicon(SiGe BiCMOS or Si CMOS) even in macro base stations? |
12.8. | Major technological change: from broadcast to directional communication |
12.9. | Solving the high power loss at high frequency challenge: FEMTO AND PICOCELLS |
12.10. | Analog vs Digital Beam Forming |
12.11. | Hybrid beamforming |
12.12. | Planar vs non-planar antenna array designs |
12.13. | Mobile phone (receiver) vs base station architecture |
12.14. | The common Quad structure found in Satcom |
12.15. | Example from satellite and phased-array radar: 768-ement array |
12.16. | Example from satellite and phased-array radar: 256-element Ku-band SATCOM |
12.17. | IDT (Renesas) has a strong position in beam-forming ICs |
12.18. | IDT (Renesas) 28Ghz 2x2 4-channel SiGe beamforming IC |
12.19. | NXP: 4-channel Tx/Rx beamforming IC in SiGe with low EVM |
12.20. | 28GHz all-silicon 64 dual polarized antenna |
12.21. | Anokiwave: Tx/Rx 4-element 3GPP 5G band all in silicon |
12.22. | Anokiwave: 256-element all-silicon array |
12.23. | Sivers IMA: dual-quad 5G dual-polarized beam forming IC |
12.24. | Analog: a 16-channel dual polarized beam-forming IC? |
12.25. | SoC Microwave: single-channel GaAs HEMT devices |
Slides | 258 |
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Forecasts to | 2040 |