08 Sep 2021
5 minute read

Breaking the von Neumann bottleneck using phase-change materials

New research paves the way to the discovery of novel materials for next-generation computing.

The world’s information keeps expanding. In 2018, global data storage reached 33 zettabytes (33x1021 bytes). Put another way, it would take 33 billion one-terabyte hard drives to store one zettabyte of data.

As difficult as it may be to wrap your head around that amount of data, it’s expected to swell to 175 zettabytes by 2025. Even today, storing and extracting this increasingly massive amount of data represents a remarkable challenge in terms of accuracy, efficiency and sustainable energy cost.

IBM Research is responding to this challenge by studying new materials that could be the basis for faster, more energy-efficient architectures. One of the most mature is phase-change materials (PCMs) that store and delete information based on changes in their atomic structure from crystalline to a disordered, or amorphous, state.

My recently published research,1 produced in collaboration with the Chinese Academy of Sciences, reports, for the first time, exactly how PCMs crystallize at the molecular level.

A material change

Today’s electronic devices feature von Neumann architectures, which store information via a sequential data exchange between physically separated CPU and memory or storage units. But these architectures have limited throughput, because instructions can be carried out only one at a time and sequentially.

This von Neumann bottleneck is especially limiting for artificial intelligence and deep learning applications.

PCMs are important because they can switch very rapidly and reversibly for a virtually countless number of cycles (up to 1 trillion, or 1012, cycles).

Heating PCM crystals to transform them to their softer, more amorphous form deletes information quickly. Unfortunately, cooling PCMs down to crystallize them again—in order to store information—is at least 1,000 times slower. That’s a huge bottleneck in throughput, and a barrier to the development of next-generation electronic devices.

Inefficient energy use is another downside of PCMs. Heating them consumes a lot of energy and requires efficient thermal insulation to prevent heat loss.

My work is centered on understanding and describing what’s happening on the atomic level during these PCM transformations. The goal is to develop more efficient PCM-based storage devices and, ultimately, boosting the speed and efficiency of computing architectures.

New simulation technique reveals molecular mystery

Common theories predict the crystallization occurs as a random event initiated by the formation of a crystal-like nucleus (commonly known as critical nucleus). Using a method I introduced a few years ago,2 I showed that the critical nucleus forms from a smaller nucleus. I observed that this smaller nucleus is formed from the encounter of atoms with different mobility, which allows them to adjust in space in a very stable configuration, stable enough to resist melting and to further grow to generate the critical nucleus.

The goal is to understand which atoms in a PCM move faster and how atoms interact to build a stable nucleus. Knowing this provides us with a rational to either modify the chemical composition of the PCM favoring crystallization or to develop new technologies able to speed the crystallization process.

Breaking through the von Neumann bottleneck is far from the only application for this work. It could also spur advances in neuromorphic computing, which models the brain’s interconnected network of synapses. Faster and more efficient PCMs could fire up IBM Research’s development of new materials and devices for electronic and photonic neuromorphic computing systems.

Eventually, these novel materials could be the basis for more complex AI that “thinks” like the human brain.


08 Sep 2021





  1. Song, W., Martelli, F., Song, Z. Observing the spontaneous formation of a sub-critical nucleus in a phase-change amorphous material from ab initio molecular dynamics. Materials Science in Semiconductor Processing. Volume 136. 2021. 106102. ISSN 1369-8001.
  2. Martelli, F., Ko, H., Oğuz, E., Car, R. Local-order metric for condensed-phase environments. Phys. Rev. B 97, 064105. Published 12 February 2018.