How does DRAM manufacturing work?

China's bet on a domestic DRAM maker raises a foundational question that gets lost in the geopolitical noise: what does it actually take to manufacture DRAM at scale, and why is it so hard to catch up?

Why this matters now

DRAM — dynamic random-access memory — is the working memory in virtually every computing device, from smartphones to data center servers. Because a handful of companies control nearly all global supply, any disruption to that oligopoly ripples instantly through the entire technology stack. When a new entrant attempts to break in, it is not just a business story; it is a signal about how dependent the world's industrial base is on a single, brutally difficult manufacturing process.

How it works

DRAM stores each bit of data as a charge in a tiny capacitor paired with a transistor. When power is cut, the charge leaks away — that is the "dynamic" in the name — so the chip must constantly refresh its contents, thousands of times per second. This refresh requirement shapes everything: the cell design, the power budget, the controller logic, and ultimately the manufacturing process.

Building a DRAM chip involves hundreds of sequential photolithography steps, where circuit patterns are etched onto a silicon wafer at nanometer scale. Each step must be executed with near-perfect yield — the percentage of working chips per wafer — because errors compound. A process that runs at 85% yield per step across 300 steps produces far fewer working chips than the math naively suggests.

Silicon wafer preparation
     │
     ├─ Photolithography patterning
     │      repeated many times
     │
     ├─ Etch, deposit, and planarize
     │
     ├─ Cell array formation
     │      capacitor and transistor pairs
     │
     ├─ Yield testing and binning
     │
     └─ Packaging and qualification

Yield learning is where incumbents hold their deepest advantage. Every wafer run produces data. Engineers analyze defect maps, adjust process parameters, and incrementally improve output. This knowledge accumulates over years and is extraordinarily difficult to replicate quickly, regardless of capital available. Process generations — the shrinking of cell dimensions to pack more bits per wafer — require re-learning yield from scratch each time.

Real-world applications

Understanding DRAM manufacturing explains several dynamics that professionals encounter regularly.

AI infrastructure: Large language models and inference workloads are memory-bandwidth hungry. Data centers specify DRAM type and speed as carefully as they specify processors. High-bandwidth memory variants are simply DRAM dies stacked and connected differently — the underlying manufacturing is the same.

Supply chain risk: Because DRAM production is capital-intensive and dominated by a small number of fabs, a fire, earthquake, or export restriction at one facility can tighten global memory prices within weeks. Procurement teams managing hardware budgets feel this directly.

Product tiers: The DDR4, DDR5, and LPDDR families that appear in product specs are not marketing labels. Each generation requires a new process node and a fresh yield-learning cycle, which is why transitions take years and why a producer generations behind cannot simply license its way current.

Geopolitical dependency: Any country whose domestic technology stack relies entirely on imported DRAM has a single point of failure. Establishing domestic production — even at a generation behind — changes procurement leverage and reduces catastrophic exposure to supply shocks.

Where to go deeper

To build a solid foundation in semiconductor manufacturing logic, explore courses on chip supply chains, semiconductor business models, and hardware infrastructure for AI. Pairing that with material on supply chain strategy and technology risk gives you the framing to evaluate news like this as a systems story rather than a horse race between companies. The durable skill here is reading manufacturing capability claims critically — understanding what yield, process node, and capacity scale actually mean before accepting any IPO prospectus or analyst report at face value.