Automotive chips can be divided into four types according to their functions: control (MCU and AI chips), power, sensors, and others (such as memory). The market is monopolized by international giants. The automotive chips people often talk about refer to the computing chips in cars. According to the integration scale, they can be divided into MCU chips and AI chips (SoC chips). Power devices have low integration and belong to discrete devices, mainly including IGBT, MOSFET, etc. in electric vehicle inverters and converters. Sensors include radars and cameras on smart cars.
Automotive-grade MCU chips
Automotive-grade MCU chips are an important part of automotive electronic control units (ECUs) and are widely used in dozens of subsystems in vehicles, such as suspension, airbags, door control, etc. They are the core of internal computing and processing in automotive electronic systems. MCU chips are divided into 8-bit, 16-bit, and 32-bit MCUs according to the number of bits of data processed by the CPU at one time.
(1) 8-bit MCU: It has the advantages of simplicity, durability, and low price. It provides low-end control functions, such as fan control, air conditioning control, windshield wipers, sunroofs, window lifts, low-end instrument panels, junction boxes, seat control, door control modules, etc.
(2) 16-bit MCU: It provides terminal control functions and is used in power systems and chassis control systems, such as engine control, gear, and clutch control, electronic turbo systems, suspension systems, electronic power steering wheels, torque dispersion control, electronic pumps, electronic brakes, etc.
(3) 32-bit MCU: It has the highest operating frequency, better processing capacity and execution efficiency, and is more widely used. The price is also gradually decreasing. It provides high-end control functions and plays an important role in realizing L1 and L2 autonomous driving functions.
According to statistics, each traditional car uses more than 70 MCUs on average, while smart electric vehicles use more than 300. However, with the accelerated trend of centralization of the vehicle's electronic architecture, the usage and types of single-vehicle MCUs will also "shrink". The performance of MCUs will be further improved, and high-end MCUs will gradually replace some demands for low-end MCUs.
AI chips
AI chips are the "brains" of future intelligent cars. This kind of chip is generally a SOC chip that integrates a CPU, image-processing GPU, audio processing DSP, deep learning acceleration unit NPU, memory, and various I/O interfaces. It is different from MCUs that mainly use CPU for computing. In cars, SoC chips are mainly used in two aspects: intelligent cockpits and autonomous driving.
The multiple experiences represented by future intelligent cockpits, such as "in-vehicle infotainment system + streaming media rearview mirror + head-up display system + full LCD instrument + vehicle networking system + in-vehicle occupant monitoring system", will all depend on the SoC chip of the intelligent cockpit.
Autonomous driving chips refer to SoC chips that can achieve high-level autonomous driving. They usually have a multi-core architecture of "CPU + XPU". The central computing platform at the vehicle end of L3 and above needs to reach a computing power of 500+ TOPS. Chips with only a CPU processor cannot meet this demand. Autonomous driving SoC chips usually need to integrate one or more XPUs in addition to the CPU for AI computing. XPU for AI computing can choose GPU/FPGA/ASIC, etc.
GPU, FPGA, and ASIC each have their advantages in the field of autonomous driving AI computing: The CPU is usually the control center of the SoC chip. Its advantage lies in strong scheduling, management, and coordination capabilities, but its computing power is relatively limited. For AI computing, people usually use GPU/FPGA/ASIC for enhancement:
1) GPU is suitable for computing and processing in data-intensive applications and is especially good at processing graphical machine learning algorithms such as CNN/DNN.
2) FPGA has obvious advantages for sequential machine learning algorithms such as RNN/LSTM and reinforcement learning.
3) ASIC is a special chip designed for specific user algorithm requirements. It has the advantages of smaller size, lighter weight, lower power consumption, improved performance, enhanced confidentiality, and lower cost.
Power devices
Power semiconductor devices are semiconductor devices used for power conversion and control. Their typical application scenarios include frequency conversion, voltage transformation, current transformation, power amplification, and power management. The main types are IGBT and MOSFET. In specific applications, fuel vehicles generally use low-voltage MOSFET, and its substrate material is Si. In contrast, BEV has higher performance requirements for power devices, and IGBT and high-voltage MOSFET are more mainstream.
IGBT (Insulated Gate Bipolar Transistor) is a fully controlled voltage-driven high-power power electronic device composed of a bipolar transistor (BJT) and an insulated gate field effect transistor (MOS). The characteristics of IGBT are that it combines the advantages of low conduction voltage, large on-state current, and small loss of BJT and the high switching speed, high input impedance, small control power, and simple drive circuit of MOS.
In electric vehicles, the applications of IGBT are mainly concentrated in three aspects:
First, in the electronic control system, the IGBT module converts DC to AC to drive the vehicle motor (electronic control module).
Second, in the on-board air conditioning control system, it is responsible for low-power DC/AC inversion. The working voltage of this module is not high, and the unit price is relatively lower.
Finally, in the charging pile, the IGBT module is used as a switch.
The most common form of IGBT is a module, which is mainly composed of IGBT chips, FWD chips, main terminals, auxiliary terminals, cast packaging materials, insulating substrates, metal bases, resin outer covers, and resin casings. Multiple chips are assembled on a metal substrate in an insulated manner and packaged in a hollow plastic case. The insulating materials from the air are high-voltage silicone grease or silicone grease and other possible soft insulating materials.
From the perspective of functional safety, IGBT modules have the following advantages:
(1) Multiple IGBT chips are connected in parallel, making the current specification of IGBT larger.
(2) Multiple IGBT chips are combined according to a specific circuit form, such as half-bridge, full-bridge, etc., which can reduce the complexity of external circuit connections.
(3) Multiple IGBT chips are on the same metal substrate, which is equivalent to adding a heat spreader between the independent heat sink and the IGBT chip, making it more reliable in operation.
(4) Compared with the external connection of multiple discrete single tubes, the circuit layout of the connections between multiple IGBT chips in the module is better and the lead inductance is smaller. Therefore, the external lead terminals of the module are more suitable for high-voltage and large-current connections.
Sensor chips
Automotive sensors are mainly divided into two categories. One category is vehicle perception sensors, including speed/position sensors, low/medium voltage pressure sensors, high voltage sensors, acceleration sensors, angular velocity sensors, magnetometers, and temperature sensors. The other category is environmental perception sensors, including oxygen and gas sensors, vehicle-mounted cameras, ultrasonic radars, millimeter-wave radars, and lidars. （Click here, Position and Function of Main Automotive Sensors, there are more details)
Memory
Automotive sensor memory is divided into flash memory and memory. Flash memory includes NAND Flash and NOR Flash, and memory includes DRAM and SRAM. With the development of intelligence, the data generated by ADAS and infotainment systems will continue to increase. According to the estimate of Counterpoint Research, in the next ten years, the storage capacity of a single vehicle will reach 2TB - 11TB.
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