The Hidden Crisis of DRAM & NAND: AI Era Data Can’t Stay Stored

In the age of artificial intelligence, we have long focused on computing power, capacity, and speed. We add more DRAM, stack HBM, and expand 3D NAND to support larger models and faster inference. But a silent, dangerous crisis is emerging: data can no longer stay reliably stored.

As AI evolves from generative AI to autonomous Agentic AI, systems require persistent state, long-term memory, and continuous decision-making. They can no longer tolerate temporary or unstable data. At the same time, relentless scaling of DRAM and NAND to achieve higher density is seriously eroding data retention and error margin.

The core challenge of storage has shifted: from “can we store it?” to “can we keep it correctly?”

Core Trend: AI Makes Storage Reliability Critical

AI systems are no longer one‑off computing tasks. Modern Agentic AI relies on:

• Long-term memory
• Sustained system state
• Autonomous, continuous decision-making

This means storage must maintain accurate data over time, not just work for a short period. Reliability has become a make‑or‑break factor for AI infrastructure stability.

Root Cause: Scaling Lowers Reliability

Density improvements directly damage stability. This is an unavoidable trade-off.

For NAND Flash

• Shrunk XY dimensions
• Increased 3D stacking layers
• Result: lower error margin, easier charge loss

For DRAM

• Transition to 3D DRAM
• Smaller cell size
• Result: shorter retention time, lower noise tolerance

Rule: Higher density = Lower reliability

NAND’s Essential Problem: Charge Loss

NAND failure boils down to charge loss, which happens in two main ways:

1. Vertical charge leakage – charge escapes into the channel
2. Lateral charge diffusion – charge spreads between wordlines

Short‑Term vs Long‑Term Retention Failure

• Short term: Shallow traps, initial voltage shift (IVS), changes appear quickly
• Long term: Deep traps, combined mechanisms (TAT / DT / TE), issues grow more complex over time

DRAM’s Hidden Weakness: It Can’t “Hold” Data Either

DRAM is not safe from retention failure. It suffers from multiple leakage paths:

• Capacitor leakage
• Direct tunneling
• Subthreshold leakage & GIDL
• Junction leakage

The Fundamental Shift in Storage

Past: Storage = capacity + speed, Errors fixed with ECC

Now: Storage = long-term reliability + state consistency, Storage is the foundation of system stability

Conclusion

The real crisis in the AI era is not insufficient computing power – it is unreliable data retention.

As 3D NAND and DRAM scale to smaller geometries and higher density, charge loss and leakage worsen. AI’s demand for persistent memory amplifies these flaws.

To build stable, enterprise‑grade AI systems, the industry must shift focus from speed and capacity to retention, charge control, and long-term reliability.