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自動車用メモリチップ・ストレージ産業(2024年)

Automotive Memory Chip and Storage Industry Report, 2024

出版日: | 発行: ResearchInChina | ページ情報: 英文 500 Pages | 納期: 即日から翌営業日

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自動車用メモリチップ・ストレージ産業(2024年)
出版日: 2024年02月21日
発行: ResearchInChina
ページ情報: 英文 500 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

世界の自動車用メモリチップの市場規模は、2023年に47億6,000万米ドルであり、高度自動運転の後押しによって2028年に102億5,000万米ドルに達すると予測されています。自動車用ストレージ市場は高成長の半導体セグメントです。

自動車用メモリチップは、2023年までに自動車用半導体の約8~9%を占め、その割合は2028年に10~11%に上昇すると予測されます。主な理由は、自動車用メモリチップの技術革新が加速し、先進メモリチップの自動車への急速な採用が促進されるためです。主な促進要因は以下の通りです。

DRAM:DRAMは最大の市場規模を持つメモリチップです。コンシューマーエレクトロニクス(携帯電話、PC、タブレットPCなど)の分野では、DDR5(LPDDR5)が主流となっており、今後2~3年で従来のDDR4(LPDDR4)に徐々に取って代わる見込みです。自動車分野では、従来のDDRがDDR4、LPDDR3、LPDDR4へと進化し、さらにLPDDR5やGDDR6といった先進のストレージ製品へと進化しています。

HBM:DRAMの強化版として、HBMは複数のDRAMチップを積層することで、より高い帯域幅と容量を提供します。価格が高いため、長期にわたって自動車に搭載される可能性は低いです。しかし、TransformerのAI基盤モデルのトレーニングに使用されるクラウドサーバーは、AIサーバーコストの約9%を占める複数のHBMを搭載する必要があり、1台あたりのASP(平均販売価格)は1万8,000米ドルと高いです。

NANDフラッシュ:自動車用コンピューティングシステムでは、重要なデータや訓練されたウェイトモデルはハードディスク(iMMCやUFS)に保存されます。NANDは通常、ADAS、IVIシステム、センターコンソールなどの連続データを保存します。5ドメイン融合の動向では、今後3~5年で1台の車両に2TB超のNANDが必要になり、中央演算用の自動車用PCIe SSDが重要な成長エンジンになります。

SRAM:SRAMはNANDやDRAMよりはるかに高速ですが高価です。独立したSRAMはほとんどなくなり、主にIPカーネルの形でCPU、GPU、各種SoCに直接統合されています。高性能な自動車用SoCは、一般的に大容量のオンチップSRAMを集積しています。

MRAM:SamsungやTSMCなどのウエハー大手は、次世代自動車用途向けにMRAMを開発しています。NXPは、次世代S32ゾーンECUとMCUにMRAMを導入する予定です。業界では、MRAMがキャッシュメモリとしてSRAMに取って代わると予測されています。

EEPROM:スマートカーは最大30~40個のEEPROMチップを必要としますが、通常の燃料自動車は約15個のEEPROMチップで済みます。EEPROMは、BMS、インテリジェントコックピット、ゲートウェイ、「電気駆動、バッテリー、電気制御」システム、その他の用途に広がっています。

FRAM:FRAMは読み書きの耐久性、書き込み速度、消費電力で従来のFlashやEEPROMを凌駕し、エアバッグデータストレージ、イベントデータレコーダー(EDR)、新エネルギー車CAN-BOX、新エネルギー車通信端末(T-BOX)などの分野へ応用されています。

自動車用NANDフラッシュメモリの進化:UFS 4.0、セントラルコンピューティング用PCIe SSD、CXLメモリ拡張技術

コンピューターシステムと同様に、現在の自動車用コンピューティングシステムにもハードディスクがあり、重要なデータや学習済みのウェイトモデルが保存されています。

eMMC5.1とUFS3.1は、自動車用NANDフラッシュメモリの主流規格となっています。2024年2月、KIOXIAはAEC-Q100 Grade2要件に沿った自動車用途向けに設計された業界初のユニバーサルフラッシュストレージ(UFS)Ver.4.0組み込みフラッシュメモリデバイスのサンプル出荷を発表しました。UFS 4.0は、理論上1レーンあたり最大23.2Gbps、1デバイスあたり最大46.4Gbpsのインターフェース速度をサポートします。2025年までに、UFS 4.0が自動車用ストレージ規格の1つになり、さまざまな自動車用EEAに適用される見込みです。

当レポートでは、中国と国外の自動車用メモリチップ・ストレージ産業について調査分析し、産業の現状と見通し、競合情勢、ベンダーのプロファイルなどを提供しています。

目次

第1章 自動車用メモリチップ産業の概要

  • メモリチップの分類
  • メモリチップ産業の現状
  • 自動車用メモリチップ産業の現状
  • 自動車用メモリチップの需要と用途の見通し
  • 自動車用メモリチップ市場の競合情勢
  • 自動車用ストレージの自動車グレードの標準と認証
  • 自動車用メモリチップのローカライズ

第2章 自動車用メモリチップの開発方向

  • 自動車用ストレージの動向(I)
  • 自動車用ストレージの動向(2)
  • 自動車用ストレージの動向(3)
  • 自動車用ストレージの動向(4)

第3章 各種メモリチップの自動車への応用

  • DRAM技術と自動車への応用
  • NANDフラッシュ技術と自動車への応用
  • SRAM技術と自動車への応用
  • NORフラッシュ技術と自動車への応用
  • EEPROM技術と自動車への応用
  • FRAM技術と自動車への応用

第4章 自動車用メモリチップの応用シナリオ:セグメント別

  • 中国における乗用車の売上とインテリジェントドライビング/インテリジェントコックピット普及の予測
  • メモリチップの応用シナリオ:自動運転
  • メモリチップの応用シナリオ:コックピット
  • メモリチップの応用シナリオ:中央演算ユニット + ゾーンコントローラー
  • メモリチップの応用シナリオ:運転データ記録
  • メモリチップの応用シナリオ:クラウドコンピューティング、ストレージ

第5章 国外の自動車用メモリチップベンダー

  • Samsung
  • SK Hynix
  • Micron
  • Kioxia (Toshiba)
  • Western Digital
  • Silicon Motion
  • Fujitsu
  • Neo Semiconductor

第6章 中国の自動車用メモリチップベンダー

  • Yangtze Memory
  • CXMT
  • XMC
  • GigaDevice
  • Ingenic
  • Giantec Semiconductor
  • Puya Semiconductor
  • Fudan Microelectronics
  • Longsys
  • Macronix
  • BIWIN Storage Technology
  • Etron Technology
  • YEESTOR
  • Dosilicon
  • Konsemi
  • Mason Semiconductor
  • Rayson
  • Xi'an Unigroup Guoxin Microelectronics
  • Phison Electronics
  • Shanghai Belling
目次
Product Code: YS040

The global automotive memory chip market was worth USD4.76 billion in 2023, and it is expected to reach USD10.25 billion in 2028 boosted by high-level autonomous driving. The automotive storage market is a high-growth semiconductor segment.

Automotive memory chips accounted for about 8-9% of the value of automotive semiconductors in 2023, and the proportion is expected to rise to 10-11% in 2028, mainly because the faster innovation in automotive memory chips promotes the rapid adoption of advanced memory chips into cars. Main driving forces are:

DRAM: DRAM is the memory chip with the largest market size. In the field of consumer electronics (mobile phones, PCs, tablet PCs, etc.), DDR5 (LPDDR5) has become mainstream and will gradually replace the conventional DDR4 (LPDDR4) in the next 2-3 years. In the automotive field, the conventional DDR is evolving to DDR4, LPDDR3 and LPDDR4, and then to advanced storage products such as LPDDR5 and GDDR6.

HBM: as the enhanced version of DRAM, HBM provides higher bandwidth and capacity by stacking multiple DRAM chips. Because of high price, it is unlikely to appear in vehicles for a long time, but the cloud server used for training the Transformer AI foundation model must be equipped with multiple HBMs which account for about 9% of the AI server cost, with the ASP (average selling price) per unit as high as USD18,000.

NAND Flash: In the automotive computing system, important data and trained weight models are stored in the hard disk (i.e., eMMC or UFS). NAND generally stores continuous data in ADAS, IVI systems, center consoles, etc. In the trend towards five-domain fusion, a single vehicle will need 2TB+ NAND in the next 3-5 years, and automotive PCIe SSD for central computing will become an important growth engine.

SRAM: SRAM is much faster than NAND and DRAM but more expensive. Independent SRAM has almost disappeared, and it is mainly integrated directly into CPU, GPU and various SoCs in the form of an IP kernel. High-performance automotive SoCs generally integrate high-capacity on-chip SRAM.

MRAM: Wafer giants such as Samsung and TSMC are developing MRAM for the next-generation automotive applications. NXP plans to introduce MRAM to its next-generation S32 zonal ECUs and MCUs. The industry believes that MRAM is expected to replace SRAM as cache memory.

EEPROM: a smart car requires up to 30-40 EEPROM chips, while an ordinary fuel-powered vehicle only needs about 15 EEPROM chips. EEPROM is extending to BMS, intelligent cockpits, gateways, "electric drive, battery, electric control" systems and other applications.

FRAM: FRAM outperforms conventional Flash and EEPROM in reading and writing durability, writing speed and power consumption, and has been applied to airbag data storage, event data recorder (EDR), new energy vehicle CAN-BOX, new energy vehicle communication terminal (T-BOX) and other fields.

Evolution of automotive NAND Flash memory: UFS 4.0, PCIe SSD for central computing, CXL memory expansion technology

As with a computer system, a current automotive computing system also has a hard disk where important data and trained weight models are stored.

eMMC5.1 and UFS3.1 have become the mainstream standards for automotive NAND Flash memory. In February 2024, KIOXIA announced sampling of the industry's first Universal Flash Storage (UFS) Ver. 4.0 embedded flash memory devices designed for automotive applications in line with AEC-Q100 Grade2 requirements. UFS 4.0 supports theoretical interface speeds of up to 23.2Gbps per lane or 46.4Gbps per device. It is conceivable that by 2025, UFS 4.0 will become one of automotive storage standards, and will be applicable to different automotive EEAs.

In the future, the automotive NVMe SSD based on PCIe interfaces will offer data throughput of more than 10GB/s, and the massive storage capacity will provide strong support for the next-generation intelligent automotive systems. Automotive SSD refers to the solid-state drive with PCIe as the physical layer and NVMe as the communication protocol.

NVMe supports ultra-long queues so as to greatly ease the storage bottleneck problem during parallel computing. In the era of central computing, storage should be integrated into the central computer, and PCIe is the best choice. SSD is connected to the central computing unit SoC via a PCIe switch.

JEDEC, a standard setter in the storage industry, approved the JESD312 standard in November 2022, and officially released it on December 14. JESD312 defines the specifications of interface parameters, signaling protocols, environmental requirements, packaging, and other features for a solid state drive (SSD) targeted primarily at automotive applications. Automotive SSD is directly mounted on PCB in the form of BGA, with the size not larger than 28x28 mm. It uses four PCIe 4.0 interfaces to provide a peak transmission rate of up to 8 Gb/s.

JESD312 takes into a full account the changes of automotive electronic architectures, targets software-defined vehicles and central computing architecture, and allows the storage array to be partitioned.

CXL (Compute Express Link), a storage technology based on PCIe, will be one of the important development directions of automotive storage in the future. CXL is a new open interconnect standard, and its essence is to change the original hard disk access model into the existing memory access model and exchange data in the form of memory access. CXL enables high-speed and efficient interconnect between CPU and GPU, FPGA or other accelerators, thus meeting the requirements of high-performance heterogeneous computing.

Evolution of automotive DRAM: In the era of Transformer model, GDDR6 and HBM develop rapidly, and storage cost shoots up.

The key to AI operation lies in storage instead of AI processor. 90% of the power consumption and delay of AI operation come from storage or data transfer. In 90% working conditions, the AI processor is waiting for the storage system to transfer data, and the time required by the computing system is almost negligible, so the performance of the storage system actually determines the real computing power. Wherein, the storage bandwidth can basically be equated to the performance of the storage system and the real computing power.

In the Transformer era, there are at least more than 1 billion model parameters, a model is at least 1GB, and the storage bandwidth determines whether Transformer can be run. In addition, storage dominates power consumption. According to Intel's research, when the semiconductor process reaches 7nm, an AI chip (accelerator) takes as high as 35pJ/bit to transfer data, making up 63.7% of the total power consumption.

Intelligent vehicles pose ever higher requirements for image floating-point operation. To run Transformer smoothly, the weight model should be read up to 200 times per second, so the storage bandwidth should be at least 400GB/s, 600 GB/s better. On this basis, Samsung and Micron plan to launch their own automotive GDDR6 solutions.

In Tesla HW3.0, the storage bandwidth of the first-generation FSD is only 34GB/s, which is difficult to support the next-generation foundation models. Tesla's latest self-driving brain HW4.0 therefore uses GDDR6 at all costs, 16 pieces (2GB per piece) used, with 8 each on the front and back side, plus the 4 GDDR6 chips (also 2GB) in the cockpit controller, totaling 20 pieces (40GB), and the cost is more than USD160. HW3.0 uses 8 LPDDR4 chips, and a total of 8 LPDDR4 RAMs, each with capacity of 1GB, totaling 8GB, and the cost is about USD28.

In Tesla HW4.0, GDDR6 is non-automotive-grade D9PZR provided by Micron, and the GDDR6 physical layer of is from Cadence. Rambus also provides the GDDR6 physical layer and earns approximately USD140 million in annual revenue from its storage physical layer IPs. In September 2023, Cadence acquired the SerDes and memory interface PHY IP business from Rambus Inc.

Currently, among Tesla's automotive memory chips, the 2nd-generation FSD has the highest bandwidth ranging from 448Gb/s to 1008GB/s. SK Hynix's HBM2E (H5WG6HMN6QX038R) supports minimum bandwidth of 460GB/s, with the density of 16GB. The dual-channel design allows for 920GB/s, even up to 1840GB/s, but it is still far less cost-effective than GDDR6.

GDDR6 is expected to prevail, and HBM may follow. Tesla HW 5.0 or the third-generation FSD chip may use HBM, but this is a distant future.

Table of Contents

1 Overview of Automotive Memory Chip Industry

  • 1.1 Classification of Memory Chips
    • 1.1.1 Three Categories of Storage Devices
    • 1.1.2 Classification of Memory Chips (Semiconductor Memory)
    • 1.1.3 Positions of Different Memory Units in the Computing Unit
    • 1.1.4 Type 1: Volatile Memory (RAM)
    • 1.1.5 Type 2: Non-Volatile Memory (ROM)
    • 1.1.6 Type 2: Non-Volatile Memory (ROM): Classification of Flash Memory
  • 1.2 Status Quo of Memory Chip Industry
    • 1.2.1 Memory Chips Are the Main Driving Force for the Development of the Global Semiconductor Industry in 2024
    • 1.2.2 WSTS Predicts That Global Semiconductor Sales Will Increase by 13.1% in 2024
    • 1.2.3 Global Market Breakdown by Semiconductor Type, 2023
    • 1.2.4 Storage Will Continue to Extend to More Application Scenarios in 2024
    • 1.2.5 Memory Chip Growth Forecast in 2024
    • 1.2.6 Production Reduction of Major Storage Giants in 2023
    • 1.2.7 Major Players in Memory Chip Market by Segment
    • 1.2.8 Global Memory Chip Value Chain (1)
    • 1.2.9 Global Memory Chip Value Chain (2)
    • 1.2.10 Global Memory Chip Value Chain (3)
    • 1.2.11 Composition of NAND SSD Industry Chain
    • 1.2.12 Composition of Embedded NAND(eMMC, UFS) Industry Chain
    • 1.2.13 Composition of DRAM (DDR memory) Industry Chain
    • 1.2.14 Global Major Flash OEMs (with Fab Capabilities) (1)
    • 1.2.15 Global Major Flash OEMs (with Fab Capabilities) (2)
  • 1.3 Status Quo of Automotive Memory Chip Industry
    • 1.3.1 Classification of Automotive Chips
    • 1.3.2 Global Automotive-grade Chip Market Prospect (1)
    • 1.3.3 Global Automotive-grade Chip Market Prospect (2)
    • 1.3.4 Classification and Application of Automotive Memory Chips
    • 1.3.5 Features of Automotive Memory Chip Demand
    • 1.3.6 Application of Memory Chips in Automobiles by Type (1)
    • 1.3.7 Application of Memory Chips in Automobiles by Type (2)
    • 1.3.8 Application of Memory Chips in Automobiles by Type (3)
    • 1.3.9 Global Automotive Memory Chip Market Size in 2023
    • 1.3.10 Application and Forecast of automotive memory chips in ADAS, cockpits and other scenarios
    • 1.3.11 overall technical evolution of automotive memory chips
  • 1.4 Demand for and Application Prospect of Automotive Memory Chips
    • 1.4.1 Storage Requirements of Intelligent Vehicles by Sub-module
    • 1.4.2 Sources of in-vehicle data
    • 1.4.3 Automotive Storage Technology Transformation
    • 1.4.4 Application of NAND in Automotive Market
    • 1.4.5 Requirements of L3-L5 Autonomous Driving for Bandwidth and Capacity of Automotive Memory Chips (1)
    • 1.4.6 Requirements of L3-L5 Autonomous Driving for Bandwidth and Capacity of Automotive Memory Chips (2)
    • 1.4.7 Requirements of Sensor Data for Automotive Memory Chips (1)
    • 1.4.8 Requirements of Sensor Data for Automotive Memory Chips (2)
    • 1.4.9 Event Data Recorders (EDR) Require GB Storage
    • 1.4.10 NAND Flash Memory Will Be Required in 2025 under the Trend of Multi-domain Fusion and Centralized EEA
  • 1.5 Competitive Landscape of Automotive Memory Chip Market
    • 1.5.1 Competitive Landscape of Automotive Memory Chip Market
    • 1.5.2 Competitive Landscape of Automotive Memory Chip Market at Home and Abroad (1)
    • 1.5.3 Competitive Landscape of Automotive Memory Chip Market at Home and Abroad (2)
  • 1.6 Automotive-grade Standards and Certification for Automotive Storage
    • 1.6.1 Vehicle Supply Chain Access and Certification Process for Automotive Memory Chips
    • 1.6.2 Automotive-grade Standards and Certification Specifications for Automotive Memory Chips (1)
    • 1.6.3 Automotive-grade Standards and Certification Specifications for Automotive Memory Chips (2)
    • 1.6.4 Vehicle Supply Chain Standard System Specifications for Automotive Memory Chips
    • 1.6.5 AEC-Q100 for Automotive Memory Chips
    • 1.6.6 AEC-Q100 Test Items
    • 1.6.7 ISO 26262 for Automotive Chip Supply Chain
    • 1.6.8 ISO 26262 ASIL for Automotive Chips
    • 1.6.9 Semiconductor Classification by ISO 26262
    • 1.6.10 Automotive Memory Chips Should Comply with Functional Safety
    • 1.6.11 Challenges for Automotive SSD (1)
    • 1.6.12 Challenges for Automotive SSD (2)
  • 1.7 Localization of Automotive Memory Chips
    • 1.7.1 The U.S. Restricts Exports of Advanced Computing Chips to China
    • 1.7.2 Status Quo of China's Memory Chip Supply Chain (1): Semiconductor Materials and Equipment
    • 1.7.3 Status Quo of China's Memory Chip Supply Chain (2): Design, Manufacturing, Packaging and Testing
    • 1.7.4 Status Quo of China's Memory Chip Supply Chain (3): Memory Chip IP
    • 1.7.5 Competition between Chinese Memory Chip Vendors
    • 1.7.6 Four Types of Chinese Memory Chip Vendors (1)
    • 1.7.7 Four Types of Chinese Memory Chip Vendors (2)
    • 1.7.8 Details of 30 Chinese Memory and Master Chip Vendors (1)
    • 1.7.9 Details of 30 Chinese Memory and Master Chip Vendors (2)
    • 1.7.10 Details of 30 Chinese Memory and Master Chip Vendors (3)
    • 1.7.11 Details of 30 Chinese Memory and Master Chip Vendors (4)
    • 1.7.12 Details of 30 Chinese Memory and Master Chip Vendors (5)
    • 1.7.13 Opportunities for Chinese Suppliers in the Automotive Storage Market (1)
    • 1.7.14 Opportunities for Chinese Suppliers in the Automotive Storage Market (2)
    • 1.7.15 Business Summary of Chinese Automotive-grade Memory Chip Vendors (1)
    • 1.7.16 Business Summary of Chinese Automotive-grade Memory Chip Vendors (2)
    • 1.7.17 Business Summary of Chinese Automotive-grade Memory Chip Vendors (3)
    • 1.7.18 Business Summary of Chinese Automotive-grade Memory Chip Vendors (4)
    • 1.7.19 Business Summary of Chinese Automotive-grade Memory Chip Vendors (5)

2 Development Directions of Automotive Memory Chips

  • 2.1 Trends of Automotive Storage (I)
    • 2.1.1 Introduction to HBM
    • 2.1.2 HBM, Production Process and Cost
    • 2.1.3 HBM, DRAM and GPU Packaging for AI Application
    • 2.1.4 HBM Is Mainly Applied to High-performance AI Computing Servers
    • 2.1.5 Role of HBM in Transformer AI Model
    • 2.1.6 Application of HBM in Major AI Chips Worldwide
    • 2.1.7 The Main Difference between NVIDIA H100 and H200 Is the Storage Bandwidth of HBM
    • 2.1.8 NVIDIA H20 AI Chip Specially Designed for China Competes with Huawei Ascend 910B
    • 2.1.9 SK Hynix Tops HBM Vendors
    • 2.1.10 Performance Evolution and Development History of HBM
    • 2.1.11 Iteration Trends of HBM Technology (1)
    • 2.1.12 Iteration Trends of HBM Technology (2)
    • 2.1.13 Iteration Trends of HBM Technology (3)
    • 2.1.14 Iteration Trends of HBM Technology (4)
    • 2.1.15 HBM Automotive Applications: Performance and Price of Common Automotive Memory
    • 2.1.16 HBM Automotive Applications: Frame Diagram of HBM3 Controller
    • 2.1.17 HBM Automotive Applications: Storage Bandwidth of Common Automotive SoCs
  • 2.2 Trends of Automotive Storage (2)
    • 2.2.1 Concept of Computing in Memory Technology: Computing in Memory Breaks the Traditional Von Neumann Architecture
    • 2.2.2 Computing in Memory Technical Solution in Broad Sense: Near Memory Computing (NMC), Processing In Memory (PIM), and Computing In Memory (CIM)
    • 2.2.3 PIM Is the Development Hotspot in the Next Stage
    • 2.2.4 PIM Commercialization Cases (1)
    • 2.2.5 PIM Commercialization Cases (2)
    • 2.2.6 PIM Commercialization Cases (3)
    • 2.2.7 SK Hynix's Concept: Use Memory as an Accelerator
    • 2.2.8 SK Hynix AiMX
    • 2.2.9 CXL Computing Memory of SK Hynix: FPGA Integrated with HBM
    • 2.2.10 Samsung's Concept: Near-memory Computing (NMC) Based on CXL
    • 2.2.11 Samsung's AI-oriented HBM-PIM Technology
    • 2.2.12 Real Computing In Memory
    • 2.2.13 Main Storage Media Technology Paths for Computing In Memory (CIM)
    • 2.2.14 Chinese CIM Chip Companies and Technology Paths
    • 2.2.15 Significance of Computing In Memory (CIM) to Autonomous Driving
    • 2.2.16 HOUMO.AI's First CIM Intelligent Driving Chip: Hongtu H30
    • 2.2.17 Development History and Core Technology Architecture of HOUMO.AI
  • 2.3 Trends of Automotive Storage (3)
    • 2.3.1 Purposes of PCIe
    • 2.3.2 PCIe Standard Specifications
    • 2.3.3 PCIe Architecture
    • 2.3.4 PCIe Bus with High Bandwidth and Low Latency Is an Important Direction of Automotive Storage in the Future
    • 2.3.5 CXL Storage Technology Based on PCIe Will Be Popularized in the Automotive Industry
    • 2.3.6 CXL Technology Based on PCI-e Protocol
    • 2.3.7 The Evolution of Automotive E/E Architectures Will Trigger the Demand for PCIe SSD
    • 2.3.8 How Does PCIe SSD Meet Automotive-grade Requirements?
    • 2.3.9 Application Cases of PCIe SSD
    • 2.3.10 Application of PCIe Switches amid the Automotive EEA Evolution
    • 2.3.11 Trends of PCIe Switches
  • 2.4 Trends of Automotive Storage (4)
    • 2.4.1 Classification of NVM Technologies
    • 2.4.2 Performance of NVM
    • 2.4.3 MRAM Has the Potential to Become the Next-Generation Storage
    • 2.4.4 Embedded and Independent NVM Technology Will Develop Rapidly
    • 2.4.5 Status Quo of MRAM Players in the World and China
    • 2.4.6 TSMC Develops SOT MRAM
    • 2.4.7 Samsung Promotes the Development of MRAM Technology based on Advanced MTJ Process
    • 2.4.8 Automotive Application Cases of MRAM

3 Automotive Application of Various Memory Chips

  • 3.1 DRAM Technology and Its Application in Automobiles
    • 3.1.1 Technical Principle of DRAM
    • 3.1.2 Three Development Directions of DRAM
    • 3.1.3 DDR and Low-power LPDDR
    • 3.1.4 DDR/LPDDR Memory Standards
    • 3.1.5 Competitive Landscape of Global DRAM Market in Q3 2023
    • 3.1.6 Competitive Landscape of Global DRAM Market in Q2 2023
    • 3.1.7 DRAM Products and Technical Trends of OEMs
    • 3.1.8 Technical Competition in 3D DRAM
    • 3.1.9 Automotive Application of DRAM: Demand and Content-per-car value
    • 3.1.10 Automotive Application of DRAM: Demand for Automotive DRAM Capacity Is Constantly Increasing
    • 3.1.11 Automotive Application of DRAM: Competitive Landscape of Automotive DRAM Market
    • 3.1.12 Evolution of Automotive DRAM Technology
    • 3.1.13 Automotive Application Cases of DRAM
    • 3.1.14 Automotive-grade DRAM Products of Chinese Vendors (DDR)
    • 3.1.15 Automotive-grade DRAM Products of Foreign Vendors (LPDDR)
    • 3.1.16 Automotive-grade DRAM Products of Chinese Vendors (LPDDR)
    • 3.1.17 Automotive-grade DRAM Products of Foreign Vendors (GDDR)
  • 3.2 NAND Flash technology and automotive application
    • 3.2.1 classification and technical features of four NAND flash technologies.
    • 3.2.2 purposes of NAND Flash with different architectures
    • 3.2.3 Architecture of NAND Flash Memory
    • 3.2.4 Evolution of Automotive NAND Technology (1)
    • 3.2.5 Evolution of Automotive NAND Technology (2)
    • 3.2.6 Evolution of Automotive NAND Technology (3)
    • 3.2.7 Evolution of Automotive NAND Technology (4)
    • 3.2.8 Evolution of Automotive NAND Technology (5)
    • 3.2.9 Evolution of Automotive NAND Technology (6)
    • 3.2.10 Competitive Landscape of Global NAND Flash Market in Q3 2023
    • 3.2.11 Competitive Landscape of Global NAND Flash Market in Q2 2023
    • 3.2.12 NAND Products and Technology Trends of Storage Giants
    • 3.2.13 Summary of Products and Applications of Chinese NAND Flash Vendors
    • 3.2.14 Classification of NAND Flash Products
    • 3.2.15 XXX Has Become the Mainstream Standard of Automotive NAND Flash Memory
    • 3.2.16 SSD Standard: JESD312
    • 3.2.17 Automotive Application Trends of NAND
    • 3.2.18 Automotive Application of NAND: Competitive Landscape of Global Automotive NAND Market
    • 3.2.19 Automotive-grade eMMC Products of Domestic and Foreign Vendors
    • 3.2.20 Automotive-grade UFS Products of Domestic and Foreign Vendors
    • 3.2.21 Automotive-grade HBM Products of Domestic and Foreign Vendors
    • 3.2.22 Automotive-grade SSD Products of Domestic and Foreign Vendors
    • 3.2.23 Automotive-grade NAND Flash Products of Domestic and Foreign Vendors
  • 3.3 SRAM Technology and Its Application in Automobiles
    • 3.3.1 Technical Principle of SRAM
    • 3.3.2 Technical Features, Clock Frequency and Power Consumption of SRAM
    • 3.3.3 Competitive Landscape of Global SRAM Market
    • 3.3.4 In-vehicle Application Cases of SRAM
  • 3.4 NOR Flash technology and automotive application
    • 3.4.1 Technical Principle of NOR Flash
    • 3.4.2 Technical features of NOR Flash
    • 3.4.3 Competitive Landscape of Global NOR Flash Market (1)
    • 3.4.4 Competitive Landscape of Global NOR Flash Market (2)
    • 3.4.5 NOR Flash finds a huge application space in ADAS.
    • 3.4.6 Solutions of GigaDevice
    • 3.4.7 Automotive-grade NOR Flash Products of Domestic and Foreign Vendors (1)
    • 3.4.8 Automotive-grade NOR Flash Products of Domestic and Foreign Vendors (2)
  • 3.5 EEPROM Technology and Its Application in Automobiles
    • 3.5.1 technical principle and classification of ROM
    • 3.5.2 Technical Advantages of EEPROM
    • 3.5.3 Comparison between Three Mature Technologies in the Field of NVM Chips
    • 3.5.4 Global EEPROM Market Size
    • 3.5.5 Competitive Landscape of Global EEPROM Market
    • 3.5.6 Broad Automotive Application Prospect of EEPROM
    • 3.5.7 Application Scenarios of EEPROM in Automobiles
    • 3.5.8 Chinese Vendors Speed Up the Layout in the Relatively Small Global EEPROM Market
    • 3.5.9 Competitive Landscape of Global Automotive EEPROM Market (1)
    • 3.5.10 Competitive Landscape of Global Automotive EEPROM Market (2)
    • 3.5.11 Automotive-grade EEPROM Products of Chinese Vendors
    • 3.5.12 Application of EEPROM
  • 3.6 FRAM Technology and Its Application in Automobiles
    • 3.6.1 Technical Advantages of FRAM
    • 3.6.2 Automotive Application Scenarios of FRAM
    • 3.6.3 Application of FRAM in VCU

4 Application Scenarios of Automotive Memory Chips by Segment

  • 4.1 Prediction for Passenger Car Sales and Intelligent Driving/Intelligent Cockpit Penetration in China
    • 4.1.1 Prediction for Autonomous Driving System Installation Rate of L1/L2/L2+/L2++/L2+++/L3-L4 Passenger Cars (Robotaxis) in China
    • 4.1.2 Prediction for Passenger Car Sales and Intelligent Driving Penetration in China, 2024-2027
    • 4.1.3 Passenger Car ADCU Shipments in China (10,000 Units), 2024-2027E
    • 4.1.4 Intelligent Cockpit Domain Controller Shipments in China (10,000 Units), 2024-2027E
  • 4.2 Memory Chip Application Scenario: Autonomous Driving
    • 4.2.1 ADAS Storage Requirements Generated by ADAS Sensors
    • 4.2.2 Data Storage Requirements of L4 Autonomous Vehicles
    • 4.2.3 Local Data Storage Requirements of Different Autonomous Driving Levels (GB)
    • 4.2.4 Autonomous Driving Data Flow and Types
    • 4.2.5 Autonomous Driving Hierarchical Storage Solutions
    • 4.2.6 Storage Capacity Requirements of Different Autonomous Driving Levels
    • 4.2.7 Types of Memory for Intelligent Driving Systems
    • 4.2.8 Memory for Intelligent Driving Systems
  • 4.3 Memory Chip Application Scenario: Cockpit
    • 4.3.1 Intelligent Cockpit Storage Requirements: memory chips must meet automotive regulations
    • 4.3.2 Types of Memory for Intelligent Cockpit Systems
    • 4.3.3 Intelligent Cockpit Storage Requirements: the reading speed should be faster, and the memory chip interface of the Intelligent cockpit should be gradually upgraded to UFS (1)
    • 4.3.4 Intelligent Cockpit Storage Requirements: the reading speed should be faster, and the memory chip interface of the Intelligent cockpit should be gradually upgraded to UFS (2)
    • 4.3.5 Intelligent Cockpit Storage Requirements: Comparison between eMMC and UFS (1)
    • 4.3.6 Intelligent Cockpit Storage Requirements: Comparison between eMMC and UFS (2)
    • 4.3.7 Intelligent Cockpit Storage Requirements: Comparison between eMMC and UFS (3)
    • 4.3.8 Intelligent Cockpit Storage Requirements: Comparison between eMMC and UFS (4)
    • 4.3.9 Intelligent Cockpit Storage Requirements: Comparison between eMMC and UFS (5)
    • 4.3.10 Intelligent Cockpit Storage Requirements: (1)
    • 4.3.11 Intelligent Cockpit Storage Requirements: (2)
    • 4.3.12 Intelligent Cockpit Storage Requirements: (3)
    • 4.3.13 Intelligent Cockpit Storage Requirements: (4)
    • 4.3.14 Intelligent Cockpit Storage Requirements: (5)
    • 4.3.15 Intelligent Cockpit Storage Requirements: (6)
    • 4.3.16 Memory for Intelligent Cockpit Systems: (1)
    • 4.3.17 Memory for Intelligent Cockpit Systems: (2)
    • 4.3.18 Memory for Intelligent Cockpit Systems: (3)
  • 4.4 Application Scenario of Memory Chips: Central Computing Units + Zonal Controllers
    • 4.4.1 "Central Computer + Zone" Solutions
    • 4.4.2 Typical Layout and Functions of Zonal Controllers
    • 4.4.3 Number and Functions of OEM Zonal Controllers
    • 4.4.4 Penetration Rate of Zonal Controllers in Chinese Passenger Cars
    • 4.4.5 Requirements of Intelligent Central Gateway Controllers for Storage
    • 4.4.6 Requirements of Zonal Controllers for Storage
    • 4.4.7 Requirements of Central Computing Units for Storage
    • 4.4.8 Memory for Zonal Controllers
    • 4.4.9 Storage Solution for Central Computing Platform System
  • 4.5 Memory Chip Application Scenario: Driving Data Recording
    • 4.5.1 Development Trend of Global EDR Standards
    • 4.5.2 Implementation Roadmap of Compulsory National EDR Standards
    • 4.5.3 Automotive EDR Storage Requirements
    • 4.5.4 Application of FRAM in EDR
    • 4.5.5 Comparison between F-RAM and EEPROM in Data Writing
    • 4.5.6 Features of FRAM (Ferroelectric RAM)
    • 4.5.7 Infineon Introduced the Industry's Highest Density Serial F-RAM
  • 4.6 Memory Chip Application Scenarios: Cloud Computing and Storage
    • 4.6.1 Automotive Cloud Storage Facilitates Data Processing in Autonomous Driving R&D
    • 4.6.2 Cloud Storage of vehicle data may face regulatory issues in "data privacy and security"
    • 4.6.3 Limitations of automotive cloud storage: Traffic Costs and Data Security Regulatory Issues
    • 4.6.4 Workflow of Autonomous Driving AI learning scenario
    • 4.6.5 Challenges for data storage in Autonomous Driving AI learning system
    • 4.6.6 XSKY's Cloud Storage solution and Efficient Data Platform for Autonomous Driving
    • 4.6.7 Autonomous Driving Distributed Storage Product of Yan Rong Tech: YRCloudFile
    • 4.6.8 Cases of WD My Cloud
    • 4.6.9 "Storage + Computing" Autonomous Driving Solution of Sugon ParaStor

5 Overseas Automotive Memory Chip Vendors

  • 5.1 Samsung
    • 5.1.1 Operation in 2023
    • 5.1.2 Storage Operation in 2023
    • 5.1.3 Roadmap of DRAM and NAND
    • 5.1.4 Automotive Storage Product Line
    • 5.1.5 Evolution Planning of eUFS
    • 5.1.6 Parameters of eMMC/eUFS Flash Memory by Generation
    • 5.1.7 Mass Production Automotive Memory Solution: UFS 3.1
    • 5.1.8 Evolution Planning of PCIe SSD
    • 5.1.9 Product Portfolio of PCIe 5.0 SSD
    • 5.1.10 Evolution Planning of GDDR Memory Chips
    • 5.1.11 Evolution Planning of LPDDR Memory Chips
    • 5.1.12 The Latest 16GB LPDDR5X+1TB UFS 3.1 Multi-chip Packaging Technology
    • 5.1.13 Evolution Planning of DDR Memory Chips
    • 5.1.14 Evolution Planning of HBM Memory Chips
    • 5.1.15 Architecture of HBM-PIM
    • 5.1.16 Strategic Cooperation with SemiDrive
  • 5.2 SK Hynix
    • 5.2.1 Operation in 2023 (1)
    • 5.2.2 Operation in 2023 (2)
    • 5.2.3 Automotive Storage Product Line
    • 5.2.4 Evolution Planning of LPDDR Memory Chips
    • 5.2.5 Automotive LPDDR5
    • 5.2.6 Evolution Planning of HBM Memory Chips
    • 5.2.7 HBM3E
    • 5.2.8 eMMC 5.1
    • 5.2.9 Evolution Planning of UFS Memory Chips
    • 5.2.10 UFS3.1
    • 5.2.11 Evolution Planning of GDDR Memory Chips
  • 5.3 Micron
    • 5.3.1 Operation in 2023
    • 5.3.2 Operation in 2023: Gross Margin
    • 5.3.3 Operation in 2023: Revenue Structure by Product
    • 5.3.4 Operation in 2023: Revenue Structure by Region
    • 5.3.5 Operation in 2023: Inventory
    • 5.3.6 Memory Chip R&D Technology Roadmap, 2023-2027E (1)
    • 5.3.7 Memory Chip R&D Technology Roadmap, 2023-2027E (2)
    • 5.3.8 Automotive Storage Product Line
    • 5.3.9 Evolution Planning of DDR Memory Chips
    • 5.3.10 Automotive-grade LPDDR5X Certified by ASIL D
    • 5.3.11 Evolution Planning of UFS Memory Chips
    • 5.3.12 Automotive-grade UFS 3.1 Is Widely Used in ADAS and IVI Systems
    • 5.3.13 Evolution Planning of LPDDR Memory Chips
    • 5.3.14 Evolution Planning of GDDR Memory Chips
    • 5.3.15 Evolution Planning of HBM Memory Chips
    • 5.3.16 Evolution Planning of PCIe SSD
    • 5.3.17 Automotive Memory Application: Rising Auto R7
    • 5.3.18 Automotive Memory Application: Li L7/L8
    • 5.3.19 Automotive Memory Application: Li L9
    • 5.3.20 Automotive Memory Application: NIO ET7
  • 5.4 Kioxia (Toshiba)
    • 5.4.1 Operation
    • 5.4.2 Automotive Storage Products
    • 5.4.3 Evolution Planning of UFS Memory Chips
    • 5.4.4 Automotive UFS 4.0 (1)
    • 5.4.5 Automotive UFS 4.0 (2)
    • 5.4.6 Automotive-grade UFS3.1 Uses BiCS Flash 3D Technology.
    • 5.4.7 Technical Features of Automotive UFS3.1 Products
    • 5.4.8 Technical Features of Automotive UFS2.1 Products
    • 5.4.9 Evolution Planning of eMMC Memory Chips
  • 5.5 Western Digital
    • 5.5.1 Operation in 2023
    • 5.5.2 Operation in 2023: Profit
    • 5.5.3 Operation in 2023: Inventory
    • 5.5.4 Automotive Storage Product Line (1)
    • 5.5.5 Automotive Storage Product Line (2)
    • 5.5.6 Automotive Storage Product Line (3)
    • 5.5.7 Evolution Planning of UFS Memory Chips
    • 5.5.8 iNAND AT EU552 UFS 3.1 (for ADAS)
    • 5.5.9 iNAND AT EU312 UFS
    • 5.5.10 Evolution Planning of eMMC Memory Chips
    • 5.5.11 iNAND AT EM132 UFS
    • 5.5.12 iNAND AT EN610
  • 5.6 Silicon Motion
    • 5.6.1 Operation
    • 5.6.2 Automotive Storage Solutions
    • 5.6.1 Automotive Storage Product Line (1)
    • 5.6.2 Automotive Storage Product Line (2)
    • 5.6.5 Evolution Planning of UFS Memory Chips
    • 5.6.6 Evolution Planning of PCIe SSD
    • 5.6.7 Automotive PCIe NVMe SSD Controllers
    • 5.6.8 Ferri Automotive Single Chip Storage Solution
  • 5.7 Fujitsu
    • 5.7.1 Product Lineup: FRAM, ReRAM and NRAM
    • 5.7.2 Technical Advantages of FRAM
    • 5.7.3 Wide Application of FRAM
    • 5.7.4 Evolution Planning of FRAM Memory Chips
    • 5.7.5 Technical Features of Automotive-grade FRAM Products
    • 5.7.6 FRAM for BMS
    • 4.5.6 FRAM for VCUs
    • 5.7.8 4Mbit High-capacity FRAM Empowers Future Cars
    • 5.7.9 Nano Random Access Memory (NRAM)
    • 5.7.10 Novel Non-volatile ReRAM
    • 5.7.11 Parameter comparison between EEPROM, NOR Flash, FRAM, NRAM and ReRAM.
  • 5.8 Neo Semiconductor
    • 5.8.1 3D DRAM Technology (1)
    • 5.8.2 3D DRAM Technology (2)
    • 5.8.3 3D DRAM Technology (3)

6 Chinese Automotive Memory Chip Vendors

  • 6.1 Yangtze Memory
    • 6.1.1 Business
    • 6.1.2 Global Market Share
    • 6.1.3 Evolution Planning of UFS Memory Chips
    • 6.1.4 UFS 3.1
    • 6.1.5 Evolution Planning of PCIe SSD
    • 6.1.6 3D NAND technology
    • 6.1.7 32-layer QLC 3D NAND
  • 6.2 CXMT
    • 6.2.1 Business
    • 6.2.2 DRAM Technology Roadmap
    • 6.2.3 Evolution Planning of LPDDR Memory Chips
    • 6.2.4 Evolution Planning of DDR Memory Chips
  • 6.3 XMC
    • 6.3.1 Business
    • 6.3.2 NOR Flash Foundry Business
    • 6.3.3 NOR Flash Products
  • 6.4 GigaDevice
    • 6.4.1 Business
    • 6.4.2 Operation in 2023: Revenue
    • 6.4.3 Operation in 2023: Revenue Structure
    • 6.4.4 Operation in 2023: Gross Margin
    • 6.4.5 Operation in 2023: Inventory
    • 6.4.6 NOR Flash Product Series
    • 6.4.7 Evolution Planning of NOR Flash
    • 6.4.8 Automotive-grade NOR Flash GD25
    • 6.4.9 Evolution Planning of DDR Memory Chips
    • 6.4.10 DRAM DDR4 Products
    • 6.4.11 Evolution Planning of NAND Flash
  • 6.5 Ingenic
    • 6.5.1 Business
    • 6.5.2 Operation in 2023: Revenue
    • 6.5.3 Operation in 2023: Revenue Structure
    • 6.5.4 Overview of Segmented Business
    • 6.5.5 Evolution Planning of LPDDR Memory Chips
    • 6.5.6 Evolution Planning of DDR Memory Chips
    • 6.5.7 Automotive-grade SRAM Products
  • 6.5.8eMMC Products
  • 6.6 Giantec Semiconductor
    • 6.6.1 Semiconductor Business
    • 6.6.2 Automotive-grade EEPROM Product Series
    • 6.6.3 Automotive-grade EEPROM GT25A1024A
    • 6.6.4 Application of Automotive-grade EEPROM
    • 6.6.5 Automotive-grade SPI NOR Flash Memory Chip
    • 6.6.6 SPD Product Series
  • 6.7 Puya Semiconductor
    • 6.7.1 Business
    • 6.7.2 Operation in 2023: Revenue
    • 6.7.3 Operation in 2023: Profit
    • 6.7.4 Operation in 2023: Operation
    • 6.7.5 Automotive-grade NOR Flash Product Line
    • 6.7.6 Automotive-grade NOR Flash: PY25Q32HB-SUH-AR
    • 6.7.7 Automotive-grade EEPROM Product Line
  • 6.8 Fudan Microelectronics
    • 6.8.1 Business
    • 6.8.2 Operation in 2023: Revenue
    • 6.8.3 Operation in 2023: R&D Investment
    • 6.8.4 Storage Product Line
    • 6.8.5 Automotive-grade FM EEPROM Product Roadmap
    • 6.8.6 Automotive FM EEPROM Product Planning
    • 6.8.7 Automotive-grade EEPROM Memory Chip: FM24C512DA1
    • 6.8.8 Automotive-grade FM24C/FM25 EEPROM in Line with AEC-Q100
    • 6.8.9 Automotive-grade NOR Flash Product Line
    • 6.8.10 Automotive NOR Flash Product Planning
    • 6.8.11 Automotive NAND Flash Product Route
    • 6.8.12 Automotive NAND Flash Product Planning
  • 6.9 Longsys
    • 6.9.1 Profile
    • 6.9.2 Operation in 2023: Revenue
    • 6.9.3 Operation in 2023: Gross Margin
    • 6.9.4 Operation in 2023: Revenue Structure
    • 6.9.5 Operation: Proportion of suppliers
    • 6.9.6 Operation in 2023: Inventory and Turnover Rate
    • 6.9.7 Product Lines
    • 6.9.8 Automotive Memory Chip Product Line
    • 6.9.9 Evolution Planning of NAND Flash
    • 6.9.10 Self-developed Low and Medium-capacity Memory Chips
    • 6.9.11 Evolution Planning of UFS
    • 6.9.12 Automotive-grade FORESEE UFS (1)
    • 6.9.13 Automotive-grade FORESEE UFS (2)
    • 6.9.14 Evolution Planning of eMMC
    • 6.9.15 Automotive-grade FORESEEeMMC Certified by AEC-Q100
    • 6.9.16 Evolution Planning of DDR
    • 6.9.17 Improve the Layout of the Storage Industry Chain
    • 6.9.18 Self-developed Storage Master Chip and SLC NAND Flash Chip
  • 6.10 Macronix
    • 6.10.1 Business
    • 6.10.2 Operation in 2023: Revenue
    • 6.10.3 Automotive-grade NOR Flash Product Line
    • 6.10.4 Automotive-grade NAND Product Line
  • 6.11 BIWIN Storage Technology
    • 6.11.1 Storage Business
    • 6.11.2 Operation in 2023: Revenue
    • 6.11.3 Operation in 2023: Net Income
    • 6.11.4 Automotive Storage Solutions (1)
    • 6.11.5 Automotive Storage Solutions (2)
    • 6.11.6 Automotive Storage Product Line
    • 6.11.7 Evolution Planning of LPDDR
    • 6.11.8 Evolution Planning of uMCP
    • 6.11.9 Automotive Storage C1008 Series (1)
    • 6.11.10 Automotive Storage C1008 Series (2)
    • 6.11.11 Automotive Storage C1008 Series (3)
    • 6.11.12 Automotive Storage C1008 Series (4)
  • 6.12 Etron Technology
    • 6.12.1 Automotive Storage Products
    • 6.12.2 Application of Automotive Memory
  • 6.13 YEESTOR
    • 6.13.1 Automotive-grade eMMC Memory Chips
    • 6.13.2 Industrial-grade and Automotive-grade eMMC
    • 6.13.3 Features of Automotive-grade eMMC Memory Chips
    • 6.13.4 Automotive-grade CXL SSD
  • 6.14 Dosilicon
    • 6.14.1 Memory Chips
    • 6.14.2 Memory Chip: NAND
    • 6.14.3 Memory Chip: DRAM
    • 6.14.4 Memory Chip: NOR Flash
    • 6.14.5 Automotive-grade Storage Layout
    • 6.14.6 Core Competence
  • 6.15 Konsemi
    • 6.15.1 Profile
    • 6.15.2 Major Customers
  • 6.16 Mason Semiconductor
    • 6.16.1 Profile
    • 6.16.2 eMMC
    • 6.16.3 Self-built Packaging Factories
  • 6.17 Rayson
    • 6.17.1 Profile
    • 6.17.2 Automotive-grade Storage: LPDDR4/ 4X
    • 6.17.3 Automotive-grade Storage: eMMC
  • 6.18 Xi'an Unigroup Guoxin Microelectronics
    • 6.18.1 Profile
    • 6.18.2 CXL Memory Expansion Master Control Technical Solution
    • 6.18.3 Embedded DRAM Technology (SeDRAM®) Technology Solution
    • 6.18.4 DRAM KGD Solution
    • 6.18.5 Automotive-grade Memory Chip Solutions
    • 6.18.6 Automotive-grade Ultra-low-power LPDDR4X Memory
    • 6.18.7 Automotive-grade DDR3
  • 6.19 Phison Electronics
    • 6.19.1 Operation in 2023: Revenue
    • 6.19.2 PCIe 4.0 SSD E22T Automotive Storage Solution
  • 6.20 Shanghai Belling
    • 6.20.1 Automotive-grade EEPROM Products