MRAM is gaining attention as a next-generation memory that combines high speed and non-volatility, and its use in in-memory computing is being actively explored. This article explains the principles and characteristics of MRAM, compares it with FRAM (Ferroelectric RAM, FeRAM), and discusses its challenges and future outlook in detail.
What Is MRAM? — Overview of a Next-Generation Non-Volatile Memory
MRAM (Magnetoresistive Random Access Memory) is a type of non-volatile memory that can retain data even when power is turned off, while also offering high-speed operation and high endurance. It combines the advantages of conventional DRAM and NAND flash memory, and also provides excellent power efficiency, making it a good option for next-generation computing systems and embedded applications. This section explains the operating principles of MRAM, its technological variations, and market trends, and clarifies why this technology is considered suitable for in-memory computing.
Structure and Operating Principle of MRAM: Recording Using Spin Torque
MRAM Operating Mechanism
MRAM (Magnetoresistive Random Access Memory) is a type of non-volatile memory that uses magnetic tunnel junction (MTJ) elements to retain information. Recording is performed by controlling the direction of electron spin, and data is retained even when power is turned off. To be more precise, in the STT (Spin-Transfer Torque) method, magnetization is reversed by write current. Read operations use the tunnel magnetoresistance (TMR) effect, detecting changes in resistance between parallel and antiparallel magnetization states. This structure combines the high speed of volatile memory with the non-volatility of flash memory, making it suitable for next-generation high-performance computing and embedded systems.
Characteristics and Evolution of STT-MRAM and SOT-MRAM
STT-MRAM has already been commercialized and is increasingly adopted in embedded applications and cache memory. SOT (Spin-Orbit Torque)-MRAM is a next-generation variant that allows separation of write and read paths, enabling higher endurance and lower latency compared to the STT method. In addition, SOT offers faster switching speed and has the potential to reduce write errors. At the research stage, switching operations of less than a few nanoseconds have been reported, making it promising for in-memory computing (IMC) applications such as AI inference and real-time control. However, optimization of write current and integration density remains a key challenge.
Technical Advantages and Market Trends of MRAM
MRAM combines high speed and high endurance, and can play a complementary role to DRAM and NAND. It offers access speeds comparable to DRAM and write endurance exceeding that of flash memory. Moreover, its non-volatility allows standby power consumption to be reduced to nearly zero. In the market, major companies such as Samsung Electronics, TSMC, and GlobalFoundries have established manufacturing technologies and are deploying products for embedded processors and automotive ECUs. Furthemore, prototypes supporting in-memory computing are being developed with applications in AI accelerators and edge devices in mind. From 2025 onward, mass production and cost reduction of SOT-MRAM are expected to be key factors for wider adoption.
Memory Technologies Required for In-Memory Computing
In-memory computing (IMC) is a new computing approach designed to address issues in conventional computer architectures, such as data transfer latency and increased power consumption. By performing computations within memory, it reduces the large volume of data transfer between the CPU and memory, significantly improving both performance and energy efficiency. The following section details the background behind the emergence of IMC, its operating principles, and the requirements it places on memory technologies, and examines whether MRAM can meet these requirements.
The Von Neumann Bottleneck and the Emergence of IMC
Von Neumann Architecture vs. In-Memory Computing
In conventional Von Neumann computer architectures, there is the “Von Neumann bottleneck” which is where data transfer between the CPU and memory becomes the limiting factor in performance. As data volume increases, transfer latency and power consumption rise sharply, reducing efficiency especially in AI inference and large-scale data processing. In-memory computing (IMC) is an approach that aims to address this bottleneck by performing computation within or near the memory. By executing operations such as addition, multiplication, and logical processing directly in memory, data movement can be minimized, allowing both high performance and low power consumption to be achieved. With the evolution of new computing architectures, non-volatile memory is gaining increasing attention.
The Role of Memory in In-Memory Computing
In IMC, memory is not only a medium for data storage, but also an active component that performs part of the computation. In conventional SRAM and DRAM, separate logic circuits are required for processing, but with non-volatile memory such as MRAM, data can be retained even after power is turned off, enabling easy task resumption and immediate recovery from low-power modes.
When it comes to CIM (Computing-In-Memory), analog computation is performed directly within memory cells, the physical properties of the memory have a direct impact on computational accuracy and speed. As a result, the design of the memory device itself becomes a key factor in overall system performance.
Can MRAM Meet the Requirements of IMC?
MRAM combines low latency, high endurance, and non-volatility, meeting many of the requirements for IMC. SOT-MRAM, in particular, has the potential to achieve high switching speed and low error rates, making it suitable for applications with frequent access such as AI inference and signal processing. It also consumes less power during write operations compared to other non-volatile memories, contributing to longer battery life in edge AI and IoT devices.
However, circuit-level implementation requires careful handling of device variation and noise, and optimal integration with actual CIM architectures remains a subject for further research.
Challenges and Technical Limitations of MRAM
Although MRAM offers the advantages of a high-performance non-volatile memory, there are still technical challenges that need to be addressed for practical use. Among these challenges, reducing write current, improving energy efficiency, ensuring scalability, and lowering manufacturing cost are key issues. This section explores these challenges in detail and examines the potential for appropriate use by comparing MRAM with other non-volatile memory technologies, particularly FRAM (Ferroelectric RAM, FeRAM).
Challenges in Write Current and Energy Efficiency
While MRAM offers high performance, the amount of current required for writing remains an issue. Actually, STT-MRAM often requires several tens of microamperes per bit or more for write operations, which can be a constraint when pursuing low-power operation. As a result, as cell scaling progresses, there is concern that write power may increase relatively. Improving energy efficiency requires advances in materials and optimization of magnetization switching mechanisms. Approaches such as SOT and voltage-controlled magnetic anisotropy (VCMA) are being actively studied, and are expected to significantly reduce write power in the future.
Barriers in Scalability and Manufacturing Cost
Scaling MRAM depends on the thickness and crystalline uniformity of the magnetic tunnel junction layers. This makes process control difficult and can lead to reduced yield and increased manufacturing cost. In high-density designs, issues such as magnetic interference between cells and reduced thermal stability become more significant. Although integration with existing CMOS processes is improving, MRAM still lags behind DRAM and NAND in production scale, putting it at a disadvantage in terms of cost competitiveness. For mass production, it is essential to establish stable manufacturing processes and shorten production steps, particularly by major foundries.
Comparison with FRAM: Alternative Potential and Use-Case Optimization
FRAM offers extremely low write power, fast access, and endurance exceeding 10 trillion write cycles, making it a highly attractive option for applications that require low power consumption. In particular, for battery-powered IoT devices, industrial sensors, and automotive control systems—where both data retention and frequent updates are required—FRAM can be more efficient than MRAM. On the other hand, FRAM faces challenges in high-density integration and large-capacity scaling, making it less suitable for storage applications and large-scale cache. MRAM has advantages in those areas, but in systems where low capacity and low power are the top priorities, FRAM stands out. The optimal solution lies in adopting a hybrid configuration that combines multiple types of non-volatile memory depending on the application.
Comparison of Memory Technologies: MRAM vs. FRAM vs. DRAM
Conclusion: The Future of MRAM and the Outlook for the In-Memory Computing Era
MRAM is one of the leading candidate technologies for the next generation of in-memory computing. While there are still challenges to overcome, such as manufacturing cost and optimization of circuit implementation, its high speed and durability remain strong advantages. The following paragraph reviews industry development trends and future application areas, and discusses the criteria design engineers can use when evaluating implementation, as well as the future outlook for non-volatile memory and in-memory computing.
Industry Development Trends and Future Technological Evolution
As of 2025, semiconductor manufacturers such as Samsung Electronics and TSMC are accelerating mass production of SOT-MRAM and prototyping MRAM designed for IMC applications. While its use as a low-power, high-reliability memory for edge AI, automotive, and IoT devices continues to expand, overcoming challenges related to write current and manufacturing cost remains a key focus. Further performance improvements are expected through advances in materials and the introduction of new magnetization control technologies.
Criteria for Implementation Decisions for Design Engineers
Design engineers need to evaluate factors such as application access frequency, power consumption requirements, and endurance requirements in a comprehensive manner when deciding whether to adopt MRAM, especially for IMC applications where it is essential to incorporate measures to address computational accuracy and cell variation. In addition, comparing with existing memory technologies and considering hybrid configurations can be effective. Strategic decision-making to identify the optimal solution for each application is required.
The Future of In-Memory Computing and Non-Volatile Memory
The widespread adoption of IMC has the potential to fundamentally change the role of memory. Non-volatile memories with strong write performance, such as MRAM and FRAM, can enable low-power, high-performance computing platforms by integrating computation and data storage. Going forward, close collaboration between circuit design, architecture, and materials science is expected to drive the emergence of memory-centric computing paradigms. Whether these next-generation non-volatile memories can take on a central role will depend on both technological innovation and industry adoption strategies.
RAMXEED FeRAM Product Lineup
https://www.ramxeed.com/products/feram/feram-products.html
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