A Faster Alternative For Flash Memory Is On The Horizon

IBM’s research into phase-change memory could bring faster, more reliable storage beyond today’s flash memory limits.

Earlier this month, tech giant IBM announced a more efficient way to use phase-change memory. This new method is a breakthrough with the potential to transition electronic devices from standard RAM and flash to the much faster and more reliable phase-change memory, or PCM. Phase-change memory is a type of non-volatile optical storage that manipulates the behavior of chalcogenide glass, the same method of data storage used on rewriteable Blu-ray discs. By applying an electric current to PCM cells, the material switches between amorphous and crystalline structures, creating the binary 0s and 1s used in digital storage.

In the past, PCM’s limited capacity and high cost kept it from widespread adoption. The recent discovery by IBM researchers, however, changes the equation. By operating the cells at high temperatures and observing their reactions, they managed to store three bits per cell instead of just one. Haris Pozidis, an IBM manager of non-volatile memory research, explained, “The jump is significant because at this density, the cost of PCM will be significantly less than DRAM and closer to flash.”

Applications range from full replacement of existing memory types to hybrid approaches designed to maximize speeds and minimize costs. Even cloud-based artificial intelligence applications could see major gains. Machine learning algorithms running massive datasets will benefit from lower latency and faster access. Compared to flash, which can withstand about 3,000 write cycles, PCM can endure up to 10 million cycles, making it a game-changing technology for data centers and high-demand computing environments.

Our partners over at Nexcopy provide excellent flash memory solutions and pass on the benefits of low-cost duplication to their customers. They are also closely monitoring developments in phase-change memory and preparing to develop a duplicator built specifically for PCM technology once it becomes commercially available.

Update: Where Phase-Change Memory Stands Now (2024–2025)

When IBM first showed how to squeeze three bits into a single PCM cell, it proved the concept. The question since then has been whether this technology can scale to real-world use. The work now focuses on two fronts: lowering the power and cost per bit, and finding ways to use PCM for more than just storage.

On the hardware side, engineers are getting smarter about how the material itself behaves. By shaping the current path inside each cell, they’ve cut the amount of energy needed to flip between phases. That change is critical if PCM is ever going to compete with flash on cost and density. At the same time, a lot of research is going into the “selector” component that controls each cell. Without reliable selectors, 3D PCM arrays can’t deliver the endurance and stability needed for enterprise use. The good news is progress is being made, with new materials showing they can handle billions of cycles.

Beyond memory, PCM is finding a new role in computing. Instead of moving data back and forth between DRAM and processors, companies like IBM are testing ways to run AI calculations directly inside PCM arrays. Early results show that running matrix math in-place can cut latency and power use, which is exactly what big datasets and machine learning workloads demand. If the technology keeps moving forward, PCM could act as both storage and compute in the same package.

The commercial picture is still evolving. Intel’s decision to end its Optane line slowed early deployments, but industry watchers continue to see long-term value in PCM as costs come down. Expect to see it first in applications where endurance, consistent performance, or low-latency AI matter more than absolute price. Broader adoption will hinge on whether manufacturers can keep improving selectors and building reliable multi-layer PCM stacks.

The takeaway is clear: PCM has outgrown its “lab demo” reputation. It’s showing real potential to outlast NAND, deliver predictable performance, and even reshape how we think about AI hardware. The next big step is moving from research prototypes to production-ready devices that developers can put to work without reinventing their systems from scratch.