Phase-Change Memory: Amorphization and Recrystallization of GST

Phase-change memory GST Crystallization Amorphization MLPotential Molecular dynamics

This tutorial will teach you how to:

• build a rocksalt-like GeSb₂Te₄ (GST124) crystal from scratch,

• make an amorphous GST region with a melt–quench molecular-dynamics run,

• recrystallize the amorphous region at 600 K, and

• compare crystallization kinetics across candidate materials.

Production result: screening crystallization speed

Phase-change memory uses the large conductivity contrast between amorphous and crystalline chalcogenides to store information. A fast SET operation requires rapid crystallization, while data retention requires the amorphous state to remain stable at the device operating temperature. This speed–retention trade-off is a natural materials-optimization problem.

As a simple production study, compare pristine GST124 with GST124 in which approximately 1 at.% of all atoms are Ag atoms substituted on Ge sites. For the 672-atom cell used below, replacing seven of the 96 Ge atoms gives Ag₇Ge₈₉Sb₁₉₂Te₃₈₄, or 1.04 at.% Ag. Ag-incorporated GST225 has experimentally been reported to crystallize faster and at a lower temperature than undoped GST225 [1]. The simulation tests whether the same hypothesis is visible in seeded GST124 growth.

Warning

Treat Ag acceleration as a hypothesis, not as a promised result. Before running this comparison, confirm that the selected potential supports Ag and validate representative Ag–GST liquid, amorphous, interface, and crystal configurations against reference electronic-structure calculations. A potential trained only for Ge–Sb–Te must not be extrapolated to Ag.

Use identical cell dimensions, seed thickness, thermal history, and analysis for every material. A useful pilot consists of three independent velocity seeds for pristine GST124 and three for Ag–GST124 at 600 K. A fuller screen uses 550, 600, and 650 K. Report the median and the range across replicas.

Table 15 Production results to replace before publication. TBD values are intentional placeholders.

Material	Temperature (K)	Replicas	Median t 50 (ps)	Interface velocity (m/s)	Final crystalline fraction
GST124	550	3	TBD	TBD	TBD
GST124	600	3	TBD	TBD	TBD
GST124	650	3	TBD	TBD	TBD
1.04 at.% Ag–GST124	550	3	TBD	TBD	TBD
1.04 at.% Ag–GST124	600	3	TBD	TBD	TBD
1.04 at.% Ag–GST124	650	3	TBD	TBD	TBD

Define the crystalline fraction, \(X(t)\), with one fixed classifier for all trajectories. Suitable choices include a local-environment classifier trained on crystalline and amorphous GST, or an analysis based on the alternating Ge/Sb–Te four-membered rings used in atomistic GST studies. Define \(t_{50}\) as the first time at which \(X(t)=0.5\). Because the seed at the periodic boundary presents two interfaces to the amorphous region, a simple estimate of the growth velocity is

\[v = \frac{L_z-d_\mathrm{seed}}{2t_\mathrm{grow}},\]

where \(L_z\) is the cell length in the growth direction, \(d_\mathrm{seed}\) is the seed thickness, and \(t_\mathrm{grow}\) is the time for the two fronts to fill the mobile region.

The safest first production benchmark uses no new element: repeat pristine GST124 at several temperatures and construct its crystal-growth curve. This directly probes the programming-temperature window and avoids extrapolation in chemical composition. The Ag comparison should be added only after validation.

Introduction

GST alloys can reversibly switch between an amorphous, high-resistance state and a crystalline, low-resistance state. A RESET-like thermal history melts and rapidly quenches the active region. A SET-like treatment heats the amorphous material below the melting point so that it crystallizes. Atomistic simulation does not reproduce the complete electrothermal device, but it can isolate the structural kinetics that underpin these operations.

In this tutorial, the starting crystal has the metastable rocksalt-like GST124 structure. Te occupies one rocksalt sublattice. On the other sublattice, each conventional cell contains one Ge, two Sb, and one vacancy. The resulting seven-atom cell has exactly the Ge:Sb:Te ratio 1:2:4. Replicating it 4 x 4 x 6 gives 96 formula units:

Table 16 Tutorial system

Quantity	Value
Composition	Ge96Sb192Te384
Number of atoms	672
Starting lattice constant	6.10 Å
Supercell	4 x 4 x 6
Boundary condition	three-dimensional periodic
Fixed seed	two atomic planes across the z periodic boundary
Mobile atoms	all atoms outside the fixed seed

The ordered vacancy pattern is a convenient reproducible construction, not a claim that real metastable GST124 has perfect long-range vacancy order. The mobile part loses this artificial ordering during the melt. The two fixed planes provide the crystalline template from which the quenched region can regrow. Seeded growth is both reproducible and accessible on molecular-dynamics timescales [2] [3].

The simulation sequence is:

1. build and replicate crystalline GST124,

2. define fixed_seed and mobile regions,

3. heat, melt, and quench the mobile region at constant volume,

4. start a new 600 K trajectory from the quenched coordinates, and

5. inspect the growth front and quantify crystallization.

Part A: Build Crystalline GST124

Create a conventional rocksalt cell

1. Open AMSinput: SCM → New Input

2. Switch to a periodic engine, for example →

3. Open the crystal structures tool and choose Cubic → NaCl

4. In the builder, set the two elements to Ge and Te

5. Set the cubic lattice constant to 6.10 Å

6. Click OK

7. Edit → Crystal → Convert To Conventional Cell

8. Choose View → Periodic → Periodic View Type → Repeat Unit Cell if the conventional cell is not clear

The conventional rocksalt cell contains four Ge sites and four Te sites. We will turn two Ge sites into Sb and delete one Ge site to make a vacancy.

Edit the cation sublattice

1. Select one Ge atom, then Select → Atoms of the Same → Type; confirm that exactly four Ge atoms are selected

2. Clear the selection and select any two of the four Ge atoms

3. Open Atoms → Change Atom Type and change their atom type to Sb

4. Select one of the two remaining Ge atoms and press Delete to create the vacancy

5. Confirm that the cell contains one Ge, two Sb, and four Te atoms

6. Save the input as GST124_unit_cell

Build the simulation supercell

1. Choose Edit → Crystal → Generate Super Cell…

2. Set the diagonal replication factors to 4, 4, and 6; keep all off-diagonal entries zero

3. Click OK

4. Confirm that the structure contains 672 atoms: 96 Ge, 192 Sb, and 384 Te

5. Choose Edit → Crystal → Map Atoms To (0..1)

6. Save the input as GST124_4x4x6

Part B: Define the Crystalline Seed

The two planes adjacent across the z periodic boundary form one thin crystal slab with an interface on each side. They remain fixed during both the melt–quench and recrystallization jobs.

Assign the fixed and mobile regions

1. Choose View → View Direction → Along X-axis to see the z layers from the side

2. Select all atoms in the lowest visible atomic plane near fractional z = 0

3. Hold Shift and add all atoms in the highest visible atomic plane; across the periodic boundary these are neighboring planes

4. Choose Model → Regions, create a region and name it fixed_seed

5. With fixed_seed selected, choose Select → Invert Selection

6. Create a new region and name it mobile

7. Visually confirm that the regions are disjoint and together cover all 672 atoms

8. Save the input

Part C: Set the Machine-Learning Potential

Use the same potential for amorphization, recrystallization, and all candidate comparisons.

1. Switch to the machine-learning potential engine: →

2. Set Model to QET-MatQ

3. Set Task to Molecular Dynamics

4. If the model package is not installed, open SCM → Packages and install it before continuing

5. Save the input

Part D: Amorphize the Mobile GST

We use a single MD job with a piecewise-linear temperature program. The seed remains fixed, so this produces an amorphous mobile region next to a crystalline template rather than a fully amorphous cell.

Set the MD length and output

1. Go to Model → MD

2. Set Number of steps to 170000

3. Set Time step to 1.0 fs

4. Set Initial velocities to Random at 300 K

5. Set Trajectory sampling frequency to 500 steps

6. Set Checkpoint frequency to 10000 steps

Configure the melt–quench thermostat

1. Open Model → MD… → Thermostat and add a thermostat

2. Set Type to NHC

3. Set Temperature(s) to 300 1200 1200 300 300 K

4. Set Duration(s) to 10000 40000 100000 20000 steps

5. Set Damping constant (Tau) to 100 fs

6. Set NHC chain length to 10

7. Set Region to mobile

This schedule heats from 300 to 1200 K in 10 ps, holds at 1200 K for 40 ps, quenches to 300 K over 100 ps, and holds at 300 K for 20 ps. Repeated temperature entries create the constant-temperature segments.

Fix the crystalline seed

1. Open Model → Geometry Constraints and PES Scan

2. Select one atom that belongs to fixed_seed

3. Click + and add a Fixed position constraint for the complete fixed_seed region

4. Confirm that the thermostat acts only on mobile and that fixed_seed is not thermostatted

5. Save the job as GST124_melt_quench

Run the job.

Inspect the amorphous structure

1. Open the completed job in AMSmovie

2. Go to the final trajectory frame

3. Confirm that the mobile region lacks the straight, repeating rocksalt planes of the starting structure

4. Confirm that both fixed seed planes retain their initial coordinates

5. Compare radial distribution functions for the starting and final mobile regions; the quenched structure should show broadened peaks and reduced long-range order

6. Inspect the temperature and potential-energy curves for discontinuities or drift

Warning

Visual disorder alone is not sufficient validation. The final density, coordination statistics, partial radial distribution functions, and absence of atomic overlaps should all be checked before the structure is used as an amorphous model.

Part E: Recrystallize the Amorphous Region

Start from coordinates, not an exact MD restart. This makes it possible to assign a fresh, reproducible 600 K velocity distribution while keeping the same regions and constraints.

Transfer the quenched coordinates

1. In AMSmovie, select the final frame of GST124_melt_quench

2. Choose File → Update Geometry in Input

3. In AMSinput, verify that fixed_seed and mobile still contain the intended atoms

4. Verify that the fixed-region constraint is still present

5. Save as GST124_recrystallization_600K

Configure the 600 K production MD

1. Keep Task set to Molecular Dynamics and Model set to QET-MarQ

2. In Model → MD, set Number of steps to 1000000

3. Set Time step to 1.0 fs

4. Set Initial velocities to Random at 600 K

5. Set Trajectory sampling frequency to 1000 steps

6. Set Checkpoint frequency to 100000 steps

7. In the thermostat block, set Type to NHC and Temperature(s) to 600 K

8. Keep Tau at 100 fs, NHC chain length at 10, and Region at mobile

9. Confirm once more that fixed_seed is fixed and is excluded from the thermostat

10. Save the input

Run and monitor crystallization

1. Start the calculation with File → Run

2. Open SCM → Movie after the first trajectory frames are available

3. View the cell along x or y so that the growth direction is vertical

4. Play the trajectory and look for ordered layers propagating from both sides of the periodic seed slab

5. Plot potential energy and temperature versus time

Tip

A persistent fall in potential energy often accompanies crystallization, but energy alone is not a crystalline-fraction metric. Always compare it with a structural analysis.

Part F: Analyze the Recrystallization

GUI checks

1. In AMSmovie, compare frames at 0, 50, 100, 200, 500, and 1000 ps

2. Confirm that ordering begins at the seed interfaces rather than appearing only as isolated crystallites in the interior

3. Graph MD Properties → Potential Energy and identify the main energy-drop interval

4. Graph MD Properties → Temperature and confirm stable sampling around 600 K

5. Compare the mobile-region radial distribution function at the start and end

6. Record the first frame at which the two growth fronts meet, if this occurs

Quantitative production analysis

For a publication-quality comparison, calculate the same \(X(t)\) for every frame and every replica. Store at least the following values:

Table 17 Recommended output table

Field	Meaning
material	GST124 or candidate composition
temperature_K	Recrystallization thermostat temperature
rng_seed	Initial-velocity random seed
n_atoms	Total atom count
seed_atoms	Number of fixed seed atoms
t50_ps	First time at which \(X(t)=0.5\)
t_complete_ps	Time at which both fronts meet or a stated threshold is reached
growth_velocity_m_per_s	Velocity obtained from the interface displacement
final_crystalline_fraction	\(X(t)\) averaged over the final analysis window
status	Completed / censored / unstable

If a run does not reach 50% crystallinity within 1 ns, report \(t_{50}\) as right-censored (>1000 ps), not as a failure or a fabricated extrapolation.

Part G: Materials-Optimization Extension

Pilot comparison: Ag–GST124

This branch is optional and should be attempted only after the selected model has been validated for Ag-containing GST.

1. Reopen the pristine GST124_4x4x6 crystal before amorphization

2. Select seven Ge atoms distributed through the mobile region; do not replace atoms in fixed_seed

3. Use Atoms → Details (Color, Radius, Mass…) to change those atoms from Ge to Ag

4. Confirm the composition Ag₇Ge₈₉Sb₁₉₂Te₃₈₄

5. Save as Ag7_Ge89_Sb192_Te384

6. Repeat the complete melt–quench so the Ag-containing amorphous structure has the same thermal history

7. Repeat the 600 K recrystallization with at least three independent velocity seeds

8. Compare \(t_{50}\), interface velocity, final crystalline fraction, and trajectory stability with pristine GST124

Use different, reproducible sets of seven Ge sites to test sensitivity to the dopant placement. Do not place Ag only at the crystal–amorphous interface, because that would confound bulk composition with deliberate interface decoration.

Recommended validation gate

Before interpreting Ag trends:

• compare potential energies and forces against reference calculations for representative crystalline, liquid, amorphous, and interface snapshots,

• check that Ag coordination and diffusion are physically reasonable,

• test more than one dopant placement and more than one velocity seed, and

• verify that any apparent speed-up is larger than replica-to-replica scatter.

If Ag is not supported, use the pristine temperature sweep in Table 15 as the production case study. A second same-elements extension is to compare compositions along the GeTe–Sb₂Te₃ pseudobinary line, provided each composition is built at a physically appropriate density and analyzed with the same protocol.

Limitations

This tutorial models an idealized, periodic, seeded interface at fixed volume. It does not include electrodes, Joule heating, electronic transport, free surfaces, device-scale thermal gradients, or stress relaxation against a surrounding dielectric. The simulated interface speed is therefore a materials descriptor, not a direct prediction of a memory cell’s SET time.

Finite size, seed orientation, vacancy arrangement, quench rate, thermostat, and the chosen structural classifier can all affect the result. A production study should test these choices and should preserve all failed or censored runs in the statistical analysis.

Summary

In this tutorial, you learned how to use AMSinput and an ML potential to:

• construct a 672-atom rocksalt-like GST124 supercell from scratch,

• preserve two crystal planes as a seed,

• create a melt–quenched amorphous region,

• run seeded recrystallization at 600 K for up to 1 ns,

• inspect energy, temperature, structure, and growth-front motion, and

• turn the workflow into a controlled materials-optimization study.

The recommended first production result is a temperature-dependent GST124 growth curve. The recommended candidate-material hypothesis is approximately 1 at.% Ag substituted on Ge sites, subject to explicit potential validation.

References

[1]

J. H. Han, K. S. Jeong, M. Ahn, D. H. Lim, W. J. Yang, S. Jong Park, and M. H. Cho. Modulation of phase change characteristics in Ag-incorporated Ge2Sb2Te5 owing to changes in structural distortion and bond strength. Journal of Materials Chemistry C 5, 3973–3982 (2017).

[2]

J. Kalikka, J. Akola, J. Larrucea, and R. O. Jones. Nucleus-driven crystallization of amorphous Ge2Sb2Te5: A density functional study. Physical Review B 86, 144113 (2012).

[3]

Y. Zhou, W. Zhang, E. Ma, and V. L. Deringer. Device-scale atomistic modelling of phase-change memory materials. Nature Electronics 6, 746–754 (2023).

[4]

O. Abou El Kheir, L. Bonati, M. Parrinello, and M. Bernasconi. Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential. npj Computational Materials 10, 33 (2024).