Key Findings
SK hynix has announced the commencement of sampling for its 12-layer HBM4E (High Bandwidth Memory Extended) designed for next-generation AI systems. This groundbreaking HBM4E leverages the company’s proprietary Advanced MR-MUF (Mass Reflow-Molded Underfill) packaging technology to achieve substantial performance enhancements. Specifically, it delivers a massive 48GB capacity per stack and data transfer speeds of up to 16Gbps. Crucially, it boasts over 20% improvement in power efficiency and a 17% enhancement in thermal resistance when compared to its HBM4 predecessor, setting a new benchmark for AI memory performance and reliability.
Technical / Clinical Details
HBM4E is critical for the advancement of AI accelerators and High-Performance Computing (HPC), providing the high data bandwidth and capacity demanded by increasingly complex workloads. The 12-layer vertical stacking of DRAM dies enables the impressive 48GB capacity within a compact footprint. The Advanced MR-MUF packaging technology is a sophisticated process that involves injecting a liquid underfill material after thermal compression bonding, followed by a mass reflow process for uniform curing. This approach enhances the reliability of inter-die connections while creating an efficient thermal dissipation path.
The specific improvements achieved with Advanced MR-MUF in HBM4E include:
- Over 20% Improved Power Efficiency: Optimized internal wiring structures and superior thermal management contribute to a significant reduction in overall AI system power consumption.
- 17% Enhanced Thermal Resistance: The advanced thermal properties of the MR-MUF material and uniform underfill ensure stable operation of the memory chips under high loads, maintaining reliable performance.
- Up to 16Gbps Data Transfer Speed: With 16Gbps per pin, HBM4E alleviates data bottlenecks between the CPU/GPU and HBM, maximizing AI processing capabilities.
These characteristics are vital for significantly improving the efficiency and speed of large-scale AI model training and inference.
Background & Context
The rapid evolution of AI technology imposes unprecedented demands on semiconductor memory. The advent of generative AI, in particular, has driven the need for HBM to offer much higher bandwidth and larger capacities when paired with GPUs compared to conventional memory. As a pioneer in HBM technology, SK hynix has consistently led innovations across successive generations, including HBM2, HBM2E, and HBM3. The sampling of HBM4E marks a significant milestone in the HBM roadmap, representing a strategic move to bring next-generation technology to market ahead of competitors.
The Advanced MR-MUF technology is essential for increasing HBM stacking density and enhancing performance, playing a pivotal role in establishing SK hynix’s leadership in the HBM market. This technology effectively addresses the critical issues of thermal management and power consumption in HBM, thereby enabling the realization of even higher-performance AI chips.
Strategic Significance & Outlook
SK hynix’s sampling of 12-layer HBM4E is set to profoundly impact next-generation AI system development. Once HBM4E enters mass production following customer qualification, AI accelerators will gain access to even greater processing power and efficiency. This will facilitate the execution of larger and more complex AI models, accelerating the evolution of AI applications in sectors such as autonomous driving, high-performance computing, and data centers.
For researchers and engineers, the introduction of HBM4E opens new possibilities for designing novel architectures and realizing more ambitious AI projects that were previously unattainable. For investors, it serves as a strong signal that SK hynix maintains technological leadership in the AI memory market and is well-positioned to capture future growth opportunities. HBM4E is unequivocally set to increase its prominence as a primary driver of performance improvement in the AI-driven semiconductor industry.
Source: https://advancedpackaging.news/article/124523/SK_hynix_ships_HBM4E_samples
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