Million-atom simulation of the set process in phase change memories at the real device scale

TL;DR

This paper demonstrates large-scale atomistic simulations of phase change memory materials using neural network potentials, revealing detailed insights into nucleation, growth, and defect distribution at device-relevant scales.

Contribution

It introduces a neural network potential enabling million-atom simulations of phase change processes, surpassing previous DFT limitations and capturing real device conditions.

Findings

Visualization of crystal nucleation and growth dynamics.

Quantification of defect distributions affecting electronic transport.

Simulation of processes at scales matching actual memory devices.

Abstract

Phase change materials are exploited in several enabling technologies such as storage class memories, neuromorphic devices and memories embedded in microcontrollers. A key functional property for these applications is the fast crystal nucleation and growth in the supercool liquid phase. Over the last decade, atomistic simulations based on density functional theory (DFT) have provided crucial insights on the early stage of this process. These simulations are, however, restricted to a few hundred atoms for at most a few ns. More recently, the scope of the DFT simulations have been greatly extended by leveraging on machine learning techniques. In this paper, we show that the exploitation of a recently devised neural network potential for the prototypical phase change compound Ge$_2$Sb$_2$Te$_5$, allows simulating the crystallization process in a multimillion atom model at the length and time scales of the real memory devices. The simulations provide a vivid atomistic picture of the subtle interplay between crystal nucleation and crystal growth from the crystal/amorphous rim. Moreover, the simulations have allowed quantifying the distribution of point defects controlling electronic transport, in a very large crystallite grown at the real conditions of the set process of the device.