Samsung Sees Rebound in Foundry and DRAM

Samsung's semiconductor business is rebounding, with foundry utilization surpassing 80% in the first quarter of 2026. The company's DRAM revenue also jumped by 40% in the fourth quarter of 2025, driven by its HBM4 memory strategy for AI infrastructure.

- The DRAM revenue jump in Q4 2025 allowed Samsung to reclaim the #1 market position from rival SK Hynix for the first time in a year, securing $19.2 billion in DRAM revenue. The surge was fueled by strong demand for servers and rising prices for conventional memory chips. - The foundry's utilization rate is a significant turnaround from the 50% range experienced in the second half of 2025. The recovery is attributed to new orders for mature 4nm and 8nm processes and Samsung using its own foundry to produce the base die for its HBM4 memory. - Samsung has begun mass production of its HBM4, claiming a processing speed of 11.7 Gbps, which is approximately 46% faster than the 8Gbps JEDEC industry standard. This performance is achieved by combining its 6th-generation 10nm-class DRAM with a 4nm logic base die. - Samsung's push into advanced nodes includes the world's first commercial production of 3nm Gate-All-Around (GAA) technology, which claims a 45% reduction in power consumption compared to the 5nm process. The company is now using its 2nm GAA process for its own Exynos 2600 mobile application processor. - This rebound is happening amidst significant geopolitical pressures, including U.S. export controls impacting sales to China. In response, Samsung is diversifying its manufacturing footprint with a $17 billion foundry in Taylor, Texas, which is expected to be operational by the end of 2026 and is critical for serving US clients like Tesla. - A core part of Samsung's strategy is to offer an integrated "turnkey" service combining its memory, foundry, and advanced packaging businesses to secure large-scale orders from major tech companies. - The company's Exynos line of processors, featuring integrated Neural Processing Units (NPUs), is central to its on-device AI strategy. This approach focuses on running AI models locally on devices, reducing reliance on cloud data centers.

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