Core Viewpoint - The article discusses the significant shift in the automotive MCU market with the introduction of new embedded storage technologies like PCM and MRAM, moving away from traditional embedded Flash technology. This transition is seen as a strategic move that will have a profound impact on the MCU ecosystem [1][3]. New Storage Pathways - Major MCU manufacturers such as ST, NXP, and Renesas are launching new automotive MCU products featuring advanced embedded storage technologies, indicating a shift from traditional 40nm processes to more advanced nodes like 22nm and 16nm [2]. - The evolution of MCUs is characterized by increased integration of AI acceleration, security units, and wireless modules, positioning them as central components in automotive applications [2]. Embedded Storage Technology Revolution - The rise of embedded non-volatile memory (eNVM) technologies is crucial for addressing the challenges posed by the complexity of software-defined vehicles (SDVs) and the increasing demands for storage space and read/write performance [3]. - Traditional Flash memory is becoming inadequate in terms of density, speed, power consumption, and durability, making new storage solutions essential for MCU advancement [3]. ST's Adoption of PCM - ST has introduced the Stellar series of automotive MCUs featuring phase change memory (PCM), which offers significant advantages over traditional storage technologies [5][6]. - The Stellar xMemory technology is designed to simplify the development process for automotive manufacturers by reducing the need for multiple memory options and associated costs [7][9]. NXP and Renesas Embrace MRAM - NXP has launched the S32K5 series, the first automotive MCU based on 16nm FinFET technology with integrated MRAM, enhancing the performance and flexibility of ECU programming [10]. - Renesas has also released a new MCU with MRAM, emphasizing high durability, data retention, and low power consumption, further showcasing the advantages of MRAM technology [11]. TSMC's Dual Focus on MRAM and RRAM - TSMC is advancing both MRAM and RRAM technologies, aiming to replace traditional eFlash in more advanced process nodes due to the limitations faced by eFlash technology [15]. - TSMC has achieved mass production of RRAM at various nodes and is actively developing MRAM for automotive applications, indicating a strong commitment to new storage technologies [15][16]. Integration of Storage and Computing - The article highlights a trend towards "storage-computing integration," where new storage technologies like PCM and MRAM are not just replacements but catalysts for MCU architecture transformation [19]. - The merging of storage and computing functions is becoming increasingly important in the context of AI, edge computing, and the growing complexity of computational tasks [21]. Conclusion - The MCU landscape is evolving from a focus on basic control systems to a more integrated approach where storage plays a critical role in computing architecture, driven by advancements in embedded storage technologies [23]. - This transformation presents both challenges and opportunities for domestic MCU manufacturers, who must adapt to the rapidly changing technological landscape [23].

Core Viewpoint - The semiconductor industry is shifting from a singular focus on process miniaturization to diversified innovation, with advanced packaging technologies and specialty processes driving performance optimization and differentiation in the market [1][2]. Group 1: Market Trends and Growth - The global specialty process market has surpassed $50 billion, with a compound annual growth rate (CAGR) of 15%, significantly outpacing the average growth rate of the semiconductor industry [1]. - Companies like TSMC, UMC, and SMIC are accelerating their investments in specialty processes, with TSMC establishing itself as a global benchmark through its extensive technology portfolio [2][4]. Group 2: TSMC's Specialty Process Landscape - TSMC offers a comprehensive range of specialty processes, including automotive, ultra-low power (ULP)/IoT, RF, embedded non-volatile memory (eNVM), high-voltage display, and CMOS image sensors (CIS) [4]. - TSMC's automotive-grade processes are designed for high reliability and long lifecycle, supporting advanced driver-assistance systems (ADAS) and smart cockpit applications [4]. - The N4e process is optimized for ultra-low power IoT AI devices, balancing performance and cost effectively [4]. Group 3: Innovations in Non-Volatile Memory (NVM) - TSMC is addressing the limitations of traditional eFlash technology by advancing embedded NVM technologies such as RRAM and MRAM, which are expected to replace eFlash in automotive and IoT applications [6][7]. - RRAM technology is being commercialized, with TSMC's 22nm RRAM already certified for automotive applications, and 12nm RRAM expected to follow suit [6][7]. - MRAM technology is also being developed for automotive applications, with NXP and TSMC collaborating on 16nm embedded MRAM for high-end automotive MCUs [20][21]. Group 4: Competitive Landscape and Future Directions - Major MCU manufacturers are exploring various new storage technologies, including eRRAM, eMRAM, ePCM, and eFeRAM, to enhance performance and reduce power consumption [16][31]. - The market for embedded NVM is projected to grow significantly, with wafer production expected to increase from approximately 3 KWPM in 2023 to about 110 KWPM by 2029, indicating a CAGR of around 80% [29]. - TSMC plans to integrate advanced processes with specialty technologies to support the evolution of chip architecture from "functional integration" to "system reconstruction" [8][34].

Core Viewpoint - The semiconductor industry is shifting from a singular focus on process miniaturization to diversified innovation, with advanced packaging and specialty processes becoming key drivers for performance optimization and differentiation [1][2]. Group 1: Specialty Processes and Market Growth - The global specialty process market has surpassed $50 billion, with a compound annual growth rate (CAGR) of 15%, significantly outpacing the average growth rate of the semiconductor industry [1]. - Specialty processes focus on customized and diverse process optimizations, achieving a precise balance of performance, power consumption, and cost, particularly in demanding fields like automotive electronics and IoT [1]. Group 2: TSMC's Leadership in Specialty Processes - TSMC is establishing itself as a global benchmark in specialty processes through a combination of technological breadth and ecosystem depth, expanding its capabilities across various domains including automotive and RF technologies [2][4]. - TSMC's advanced logic technologies, such as N7A, N5A, and N3A, are specifically designed for automotive applications, ensuring high reliability and long lifecycle [4]. Group 3: Innovations in Embedded Non-Volatile Memory (eNVM) - TSMC is addressing the limitations of traditional eFlash memory by advancing RRAM and MRAM technologies, which are expected to replace eFlash in automotive and IoT applications [6][8]. - The introduction of RRAM and MRAM technologies allows for significant improvements in performance, reliability, and power efficiency, with TSMC's RRAM already in mass production at 40, 28, and 22 nm nodes [7][8]. Group 4: Competitive Landscape and Future Trends - Major MCU manufacturers are collaborating with foundries to leverage specialty processes, with companies like Infineon and NXP adopting eNVM technologies to enhance their product offerings [9][16]. - The market for embedded NVM is projected to grow rapidly, with wafer production expected to increase from approximately 3 KWPM in 2023 to about 110 KWPM by 2029, indicating a strong shift towards new storage technologies [26]. Group 5: Diverse Storage Technologies - Various new storage technologies, including eRRAM, eMRAM, and ePCM, are being explored by different manufacturers, each offering unique advantages in terms of speed, power consumption, and integration capabilities [30][32]. - The trend indicates a move towards a multi-storage technology ecosystem rather than a single dominant solution, reshaping the MCU landscape in the post-eFlash era [32].

Core Viewpoint - The article discusses the innovative RRAM technology developed by "Mingxin Qirui," which integrates storage and computing to significantly enhance AI computing efficiency. The company recently completed nearly 100 million yuan in angel financing, led by Jin Qiu Fund, with participation from major strategic and financial investors like Lenovo Ventures and Xiaomi Investment [1][4]. Group 1: Company Overview - "Mingxin Qirui" was established in May 2024 and focuses on developing new RRAM storage and AI computing technologies to overcome traditional computing architecture limitations [1]. - The company has a strong foundation in intellectual property, with over 200 patents and chip design IP derived from the research team at the Chinese Academy of Sciences, which has over 20 years of systematic research in semiconductor storage [3]. Group 2: Technology Advantages - RRAM technology allows for the integration of storage and computation, addressing the "memory wall" bottleneck in traditional computing architectures, especially in AI applications that require extensive matrix operations [1][2]. - RRAM consumes significantly less energy compared to traditional storage, has the potential for higher storage density due to its ability to modulate multiple resistance states, and is expected to play a crucial role in AI computing [2]. Group 3: Market Position and Partnerships - The company is rapidly advancing its productization and commercialization efforts, having signed contracts worth several million yuan for embedded IP and strategic cooperation agreements for independent RRAM chips [4]. - Major industry players, including TSMC, Samsung, Micron, and SK Hynix, are also exploring RRAM technology, indicating a competitive landscape [3]. Group 4: Investment Insights - Investors view RRAM as a promising technology for AI computing, with Jin Qiu Fund highlighting its low power consumption, fast read/write capabilities, and high density as key advantages [5][6]. - Lenovo Ventures emphasizes the potential of RRAM to significantly reduce AI operational costs and enhance computing efficiency, aligning with the evolving demands of the AI landscape [6].