29% CAGR in wafer revenue for ADAS and autonomous applications

Semiconductors for Autonomous and Electric Vehicles 2023-2033

Semiconductor materials trends within electric and autonomous vehicles, battery management system (BMS), microcontrollers (MCU), Systems on chips (SoCs), ADAS, LiDAR, radar, 5G connectivity. Silicon (Si), Gallium Nitride (GaN), Silicon Carbide (SiC).

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The "Semiconductors for Autonomous and Electric Vehicles 2023-2033" report provides a deep dive into how megatrends in the automotive industry, such as electrification and autonomous vehicle (AV) are bringing new growth and opportunity to the semiconductor industry. These trends require new componentry on vehicles, such as LiDAR in automation, and with them new semiconductors, such as indium phosphate laser emitters. Eighteen components across the areas of advanced driver assistance systems, automated driving, electrification, communications & infotainment, and general MCU architectures are analysed for semiconductor content and trends which will impact semiconductor technologies used.
Semiconductor technology is at the heart of all modern vehicles. Their presence and prevalence have risen rapidly over the past couple of decades in the form of microcontrollers which now govern almost all aspects of vehicle operation. They communicate inputs, execute actuations and perform calculations across the entire car. This has transformed the public's perception of vehicles into "computers on wheels". Unsurprisingly, and as shown in the graphic below, automotive MCUs are the leading component for generating semiconductor value within the vehicle.
Semiconductor wafer value in the average vehicle today, and in 2033. Source: IDTechEx
IDTechEx's "Semiconductors for Autonomous and Electric Vehicles 2023-2033" report finds that the value of MCUs within vehicles is going to continue to grow. This will contribute to a wafer revenue CAGR of 9.4%, but much of the growth is going to be driven by growing semiconductor demand within advanced driver assistance systems (ADAS), autonomous vehicles (AV) and vehicle electrification. Not only will these new components require additional MCUs, but the advanced and intensive processing undertaken in automated driving is seeing the adoption of more cutting-edge semiconductor technologies into the vehicle.
Semiconductors for ADAS and autonomous vehicles, a rapidly growing sector
ADAS and autonomous vehicles promise to revolutionise the transportation industry with the superhuman safety they can offer. Providing these superhuman capabilities are a suite of sensors and computers that rely on advanced semiconductor technologies to function and provide the best performance. As such ADAS and autonomous vehicles are going to be a great boon to the automotive semiconductor market. According to the findings of this report the semiconductor wafer revenue for ADAS and AV applications will grow at a 10-year CAGR of 29%. There are three factors that combine to drive this high growth rate:
1. The emergence and adoption of SAE level 3 and SAE level 4 autonomous vehicles and the additional sensors required.
2. Automotive sensors transitioning to more advance semiconductors.
3. High performance computing coming to vehicles.
This report explains all these trends in detail and their impact on the semiconductor markets. Of particular interest is the growth of non-silicon-based semiconductor demand driven by LiDAR. Most LiDARs today operate in the near infrared (NIR) region with a typical wavelength of 905nm, which can be achieved with silicon photodetectors. However, the future of LiDAR is likely to use the shortwave infrared (SWIR) region with a typical wavelength of 1,550nm. This trend within the industry is demonstrated by the overwhelming dominance of 1,550nm LiDAR announcements shown in the chart below. Details pertaining to the superiority of 155nm LiDARs and the impact the switch has on changing demand across different semiconductor technologies are covered in this report.
Source: IDTechEx
While Tesla has been publicly anti-lidar, other key players in the autonomy space such as Waymo, Cruise, Daimler and Honda are all using LiDAR on their highly automated vehicles. In fact, as of the end of 2022 there have only been two vehicles certified for SAE level 3 use (where the driver's attention is not required under certain conditions) on the road; the Mercedes S-Class and the Honda Legend. IDTechEx is expecting many more high-end vehicles to follow the S-Class over the next 10-years, and for level 3 technologies to be widely available. This will of course drive semiconductor demand across all the sensor types and computers that are needed for these highly advanced vehicles. But another major trend in the automotive market that will impact semiconductor demand is electrification.
Semiconductors for automotive electrification
The automotive industry is under increasing pressure to decarbonise through electrification and In 2022 electric vehicles (EV) sales rose to approximately 10% of all new vehicle sales. EV's are now heading out of the early adopter phase and towards the early majority and wider spread adoption. EVs, their power electronics and their battery packs bring additional demands that drive more growth within the semiconductor industry.
OEMs are always looking for ways to extend range by making the vehicles more efficient and one avenue increasing in popularity is moving from inverters based on silicon to ones based on silicon carbide There are two factors that are pushing electric vehicle OEMs such as Tesla, Mercedes, Audi and Ford towards silicon carbide. Firstly, some OEMs are planning to transition from 400V to 800V architectures. The higher voltage reduces the amount of current required to achieve the same power, this means reduced wastage in the powertrain system from Ohmic losses and increased efficiency. Silicon carbide is much more suited to the higher voltage and is therefore the more sensible choice than Si. However, and the second reason for adoption, silicon carbide is also more efficient than Si at 400V, which is why players like Tesla are interested even though it would struggle to transition to 800V with its existing 400V supercharger network. This report covers the pros and cons of silicon, silicon carbide and the even more nascent gallium nitride, and explains how these new technologies are going to impact the semiconductor wafer market.
The trends mentioned here are forecasted over a 10-year period to 2033 giving wafer volumes, revenues and raw material demand over ADAS and AV, electrification communication and infotainment and vehicle MCUs. For even more granular detail a database with over 400 forecast lines is available with this report. It covers 18 components, across four key vehicle areas, and considering four levels of automation and three different powertrain types (ICE, EV and PHEV), providing wafer volumes, revenues, raw material demand and more.
Key aspects
Technology trends that will impact semiconductor demand
  • Supply and value chain structure
  • Analysis of technology trends from key semiconductor players and automotive strategy for keeping up with latest advancements
  • Detailed overview of where semiconductor products are used in the driver assistance and automated driving systems
  • Trends in sensors that will change the demand for different semiconductor technologies in the future
  • Trends in electrification that will shift demand from Si to SiC and GaN in the future
  • Overview of vehicle communication and in-cabin trends
Market Forecasts:
10-year granular market forecasts of
  • 300mm equivalent wafer volumes (Si, InP, GaN and more)
  • Semiconductor wafer revenue (Si, InP, GaN and more)
  • Raw material demand for Si, In, P, Ga, Ge and more
10-year market forecasts split by
  • SAE autonomous level (0-2, 3, 4, 4 - robotaxis)
  • ADAS and autonomous end-point technology (radar, LiDAR, camera, MCUs)
  • Electrification (BEV vs PHEV)
Analyst access from IDTechEx
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Further information
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Table of Contents
1.2.Report scope
1.3.Accompanying database with more than 400 forecast lines
1.4.Where semiconductors are found in vehicles
1.5.MCU count on vehicles of the future
1.6.Semiconductor makers and key terminology
1.7.Top-down wafer market size estimate
1.8.Foundry technologies over time
1.9.SAE Levels of Automation in Cars
1.10.ADAS adoption
1.11.Semiconductors in ADAS systems
1.12.Robotaxis and mobility as a service coming soon
1.13.Semiconductors in autonomous systems
1.14.Semiconductor suppliers by ADAS/AD Component
1.15.MCU characteristics
1.16.High performance computers for automotive
1.17.Computational efficiency
1.18.Radar in automotive
1.19.Radar anatomy and semiconductor impacting trends
1.20.Transceivers Semiconductor Trends: Virtual Channels
1.21.Automotive radar trending towards more advanced silicon
1.22.Automotive LiDARs
1.23.Semiconductors in LiDAR
1.24.LiDAR trends impacting semiconductor demand
1.25.Cameras in ADAS and AV systems
1.26.300mm silicon wafers forecast for ADAS and AV - 2023-2033
1.27.Non silicon semiconductors for ADAS and AV forecast - 2023-2033
1.28.Semiconductor wafer revenue for ADAS and AV applications forecast - 2023-2033
1.29.EV power electronics trends impacting semiconductors
1.30.The rise of electric vehicles- 2023-2033
1.31.Semiconductors in BMS core hardware
1.32.Vehicle automation and electrification doubles semiconductor value in vehicles
1.33.CHIPS acts to improve automotive shortages
1.34.300mm semiconductor wafers for electrification forecast - 2023-2033
1.35.Revenue from semiconductor wafers in electrification forecast - 2023-2033
1.36.Total automotive semiconductor wafer demand forecast - 2023-2033
1.37.Semiconductor mineral demand forecast for automotive - 2023-2033
1.38.Semiconductor wafer revenue forecast across automotive industry - 2023-2033
1.39.24 included company profiles
2.1.1.From raw material to product
2.1.2.Supply chain
2.1.3.Making smaller chips, greater yields
2.1.4.Semiconductor value chain
2.1.5.Semiconductor company in-house abilities
2.1.6.Even split between company types
2.1.7.Company locations
2.1.8.Semiconductor foundry in-house technologies
2.1.9.Trend toward fabless tier 2s for high performance.
2.1.10.Companies outsourcing to TSMC
2.1.12.Top-down wafer market size estimate
2.1.13.Increasing supply - Infineon's new Villach site
2.1.14.Increasing supply - Texas Instruments new fab in Sherman, Texas
2.2.Trends from the chip makers, next gen transistors, and technology roadmaps.
2.2.1.The drive for smaller node technologies
2.2.2.Key parameter of growth for processor and memory (1)
2.2.3.Key parameter of growth for processor and memory (2)
2.2.4.The economics of scaling
2.2.5.Foundry technologies over time
2.2.6.Routes to increase I/O density
2.2.7.Semiconductor foundries and their roadmap
2.2.8.Cutting edge for automotive
2.2.9.Transistor device development (1)
2.2.10.Transistor device development (2)
2.2.11.Key parameters for transistor device scaling
2.2.12.Evolution of transistor device architectures
2.2.13.CNTs for transistors
2.2.14.CNFET research breakthrough (1)
2.2.15.CNFET research breakthrough (2)
2.2.16.CNFET case study (1) SOC
2.2.18.On-chip memory
2.2.19.Routes to increase I/O density
2.2.20.Advanced technologies for automotive
3.1.Introduction to ADAS and AV
3.1.1.SAE Levels of Automation in Cars
3.1.2.Functions of Autonomous Driving at Different Levels
3.1.3.Adoption of ADAS (1)
3.1.4.Adoption of ADAS (2)
3.1.5.Safety Mandated Features Driving Wider Radar Adoption.
3.2.ADAS and autonomous vehicle sensor suites
3.2.1.Semiconductors in the ADAS system
3.2.2.Semiconductors in ADAS systems
3.2.3.Level 2 senor suite and semiconductors
3.2.4.Semiconductors in autonomous systems
3.2.5.SAE level 3 sensor suite and semiconductors
3.2.6.Level 4 private: The Trifactor
3.2.7.Level 4 MaaS: The Trifactor
3.2.8.ADAS products - example product page
3.2.9.Level 2 - purchasing systems not components
3.2.10.Case study - Tesla
3.2.11.Case study - Audi A8 (2017)
3.2.12.Case study - Honda Legend
3.2.13.Case study - Mercedes S-Class (2021)
3.2.14.Case study - VW Golf (2021)
3.2.15.Case study - Lexus LS, Toyota Mirai (2021)
3.2.16.Robotaxi case study - Waymo
3.2.17.Robotaxi case study - Cruise
3.2.18.Robotaxi case study - AutoX
3.2.19.Baidu/Apollo Sensor Suite
3.2.20.Aurora Sensor Suite
3.3.Semiconductors used for radar
3.3.1.Front Radar Applications
3.3.2.The Role of Side Radars
3.3.3.Radar Anatomy
3.3.4.Radar Board Trends
3.3.5.The trend towards smaller transistors
3.3.6.Transceivers Semiconductor Trends: Power and Noise
3.3.7.Transceivers Semiconductor Trends: Power and Noise
3.3.8.Transceivers Semiconductor Trends: Virtual Channels
3.3.9.SiGe BiCMOS
3.3.12.The Future
3.3.14.Automotive radar trending towards more advanced silicon
3.4.Semiconductors in automotive LiDAR
3.4.1.LiDARs in automotive applications
3.4.2.ADAS/AV sensor operating wavelength
3.4.3.Lidar integration positions for ADAS/AV
3.4.4.Lidar integration in lamps
3.4.5.Lidar integration in the grille
3.4.6.Lidar integration on/in the roof
3.4.7.Lidars integrated in other positions
3.4.8.Possible lidar integration and unit numbers
3.4.9.Core aspects of a LiDAR
3.4.10.Semiconductors in LiDAR
3.4.11.LiDAR trends impacting semiconductor demand
3.4.12.Automotive LiDAR semiconductors: Laser drivers
3.4.13.Automotive LiDAR semiconductors: Emitters
3.4.14.Automotive LiDAR semiconductors: Photodetector
3.5.Cameras and thermal cameras for ADAS and autonomous vehicles
3.5.1.Vehicle camera applications
3.5.2.Components of a CMOS image sensor die
3.5.3.Image sensor bare die
3.5.4.E-mirrors, an emerging camera application
3.5.5.In-cabin monitoring, an autonomous necessity
3.5.6.Performance and application trends
3.5.7.Performance attribute priorities
3.5.8.The importance of HDR in automotive(1)
3.5.9.The importance of HDR in automotive (2)
3.5.10.The importance of HDR in automotive (3)
3.5.11.The importance of HDR in automotive (4)
3.5.12.Infrared cameras for automotive applications
3.5.13.SWIR for autonomous mobility
3.5.14.NIR cameras for automotive applications
3.6.Automotive MCUs and chips for high performance computing
3.6.1.MCUs, the back-bone of modern vehicles
3.6.2.MCU count on vehicles today
3.6.3.MCU count on vehicles of the future
3.6.4.The zonal compute architecture.
3.6.5.Edge MCU case study - NXP S32K
3.6.6.Zonal and domain case study - NXP S32S
3.6.7.Edge MCU case study - Renesas
3.6.8.Domain and zonal MCU case study - Renesas
3.6.9.ADAS/AD chip case study - Renesas
3.6.10.Domain and zonal MCU case study - STM
3.6.11.MCU superchip case study - Nvidia
3.6.12.MCU superchip case study - Ambarella
3.6.13.MCU analysis
3.6.14.High performing computing in automotive (1)
3.6.15.High performing computing in automotive (2)
3.6.16.Computational efficiency
3.6.17.MCU - product table
3.7.ADAS and autonomous vehicle supply chains
3.7.1.Tier 1 supplier components
3.7.2.Tier 2 supplier components (1)
3.7.3.Tier 2 supplier components (2)
3.7.4.Semiconductor suppliers by ADAS/AD Component
3.7.5.VW and Ford In-House Chip Design
3.7.6.Stellantis Design Chips with Foxconn
3.7.7.Nvidia Autonomous Development Kit
3.7.8.Main computer supplier - Nvidia
3.7.9.Nvidia Thor - 2,000 TOPs SoC
3.7.10.Nvidia - Daimler
3.7.12.Main computer supplier - Mobileye
3.7.14.Xilinx (AMD brand)
3.7.15.Superchips to consolidate MCU market
3.7.16.Three tiers of ADAS computation architecture
3.7.17.Central computer can reduce complexity
3.7.18.A roadmap to towards full car computer architecture
3.7.19.Existing level 2/2+ Supply Chain Structure
3.7.20.Tesla's Supply Model
3.7.21.Toyota's Supply Model
3.7.22.Possible Autonomous Supply Model: Nvidia
3.7.23.Expect Supply Chain to Consolidate with Increased Automation
3.7.24.Autonomous Vertical Integration
3.7.25.Robotaxi sensor supply strategy examples
4.1.Semiconductors in power electronics: on-board chargers, dc-dc converters and inverters
4.1.1.Electric Vehicle Definitions
4.1.2.Exponential Growth in Regional EV Markets
4.1.3.Hybrid Car Outlook
4.1.4.What is Power Electronics?
4.1.5.Power Electronics in Electric Vehicles
4.1.6.Semiconductor Content Increased
4.1.7.Transistor Overview
4.1.8.Wide Bandgap (WBG) Semiconductors
4.1.9.Benchmarking Silicon, Silicon Carbide & Gallium Nitride Semiconductors
4.1.10.SiC & GaN Have Substantial Room for Improvement
4.1.11.SiC MOSFETs Vs GaN HEMTs in EV (1)
4.1.12.SiC MOSFETs Vs GaN HEMTs in EV (2)
4.1.13.SiC Power Roadmap
4.1.14.Traditional EV Inverter Package
4.1.15.Discretes & Modules
4.1.16.Electric Vehicle Inverter Benchmarking
4.1.17.SiC Drives 800V Platforms
4.1.18.Power SC Supplier Market Shares
4.1.19.SiC Supply Chain in 2023
4.1.20.Inverter Market Share 2020 - 2033: GaN 600V, Si IGBT 600V, SiC MOSFET 600V, 1200V
4.1.21.Onboard Charger Circuit Components
4.1.22.Tesla Onboard Charger / DC DC converter
4.1.23.OBC & DC-DC Converter: Si, SiC, GaN 2020 - 2033 Market Shares
4.2.Battery management system
4.2.1.Battery management system introduction
4.2.2.Block diagram of BMS - NXP
4.2.3.Block diagram of BMS - ST Micro
4.2.4.Block diagram of BMS - Infineon
4.2.5.Block diagram of BMS - generic
4.2.6.BMS core hardware
4.2.7.Example monitoring and balancing IC
4.2.8.Example microcontroller
4.2.9.Microcontroller technology
4.2.10.MCU - product table
4.2.11.Monitoring and balancing IC
4.2.12.BMS innovation
4.2.13.BMS Component Suppliers
4.2.14.Supply chain
5.1.History of connected vehicles.
5.2.Emerging connected applications
5.3.Android auto and apple car play
5.4.Case study - Volkswagen
5.5.Case study - BMW (1)
5.6.Case study - BMW (2)
5.7.The Vehicle-to-Everything vision of vehicle connectivity
5.8.Wi-Fi vs Cellular
5.9.Spectrum needs for connected vehicles (1)
5.10.Spectrum needs for connected vehicles (2)
5.11.Spectrum needs for connected vehicles (3)
5.12.4G and 5G C-V2X hardware
5.13.DSRC hardware
5.14.Screens to facilitate connected features
5.15.Infotainment hardware
6.1.EU chips act
6.1.3.The three-pillar framework on which the EU chips act are built
6.1.4.Three main elements in the EU chips Act
6.1.5.Chips for Europe initiative
6.1.6.The "first-of-a-kind" definition
6.1.7.Previous attempt
6.1.8.EU's strength and weakness in semiconductor
6.1.9.Europe's semiconductor assets
6.1.10.Time required to expand capacity or build new production line
6.1.11.Budget - Overview
6.1.12.Budget - how much is confirmed?
6.1.13.Budget - confirmed funding for "chips for EU" initiative
6.1.18.Investment information - public company (1)
6.1.19.Investment information - public company (2)
6.1.20.Investment information - public company (3)
6.1.21.Summary - 1
6.1.22.Summary - 2
6.2.US chips act
6.2.2.Key components of the US CHIPS act
6.2.3.Smaller components of the US CHIPS act
6.2.4.Breakdown of funding
6.2.5.Deeper look at CHIPS for America Fund (1)
6.2.6.Deeper look at CHIPS for America Fund (2)
6.2.7.Key themes from CHIPS for America Fund
6.2.8.Chip shortages in the automotive industry
6.2.9.CHIPS act potential beneficiaries.
6.2.10.CHIPS act to improve automotive shortages
6.2.11.US CHIPS act announcements so far - TSMC
6.2.12.US CHIPS act announcements so far - Intel
6.2.13.US CHIPS act announcements so far - Samsung
6.2.14.US CHIPS act announcements so far - GF
6.2.15.US CHIPS act announcements so far - Micron
6.2.16.Fabs vs the fabless
6.2.17.Announcements summary
6.2.18.US CHIPS act summary
7.1.1.Methodology process and report dependencies
7.1.2.Forecasting methodology
7.1.3.Components covered in this forecast
7.1.4.Accompanying database with more than 400 forecast lines
7.1.5.Key terminologies used in the forecasts
7.2.Addressable markets
7.2.1.ADAS and AV market forecast - 2023-2033
7.2.2.Electric automotive market forecast - 2023-2033
7.3.Semiconductor forecast for ADAS and autonomous vehicle applications silicon wafers forecast for ADAS and AV - 2023-2033 silicon wafers for ADAS and AV forecast split by application - 2023-2033 silicon wafers for ADAS and AV sensors forecast - 2023-2033
7.3.4.Non silicon semiconductors for ADAS and AV forecast - 2023-2033
7.3.5.Semiconductor material demand for ADAS and AV applications forecast - 2023-2033
7.3.6.Mineral demand for semiconductor in ADAS and AV applications forecast - 2023-2033
7.3.7.Semiconductor wafer revenue for ADAS and AV applications forecast - 2023-2033
7.3.8.Wafer value per vehicle forecast - 2023-2033
7.4.Semiconductors forecasts for vehicle electrification semiconductor wafers for electrification forecast - 2023-2033
7.4.2.Raw semiconductor material demand automotive electrification forecast - 2023-2033
7.4.3.Revenue from semiconductor wafers in electrification forecast - 2023-2033
7.5.Automotive semiconductor forecast grand totals
7.5.1.Total automotive semiconductor wafer demand forecast - 2023-2033
7.5.2.Total semiconductor wafer forecast for automotive, excluding MCU and other board components
7.5.3.Semiconductor mineral demand forecast for automotive - 2023-2033
7.5.4.Semiconductor wafer value per vehicle - 2023-2033
7.5.5.Semiconductor wafer revenue forecast across automotive industry - 2023-2033
8.5.Ford (ADAS)
8.6.Ford (electrification)
8.7.GaN Systems
8.8.General Motors
8.14.NXP (5G)
8.15.NXP (radar)
8.16.On Semi

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Report Statistics

Slides 358
Companies 24
Forecasts to 2033
ISBN 9781915514509

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