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A brief tutorial

Installation

To install aldenv use:

pip install aldenv

Use

aldenv implements a number of environments that can be used to benchmark optimization algorithms.

For instance:

from aldenv.envs.doseoptim import FastFast

ald = FastFast(round_to=3, noise=0.01)

creates a fast-fast ALD process where both the precursor and co-reactant are saturated after 0.2s doses and with a saturation growth per cycle of 1 Angstrom. It considers a noise level of 0.01 Angstrom and that the output is limited to three significant digits.

Sweeping the precursor dose time gives the following saturation curve:

FastFast saturation curve

doseoptim environments

doseoptim environments contain a series of virtual ALD processes where the growth per cycle is computed as a function of the precursor and the co-reactant dose times. For instance, in our work Performance of AI agents based on reasoning language models on ALD process optimization tasks, we use the following environments included in doseoptim to evaluate the ability of agents based on reasoning LLMs to optimize ALD processes:

  • FastFast represents an ideal ALD process with fast saturation for both precursor and co-reactant.
  • SlowFast represents an ideal ALD process where the precursor requires longer doses to saturate.
  • SlowSlow represents an ideal ALD process where the precursor and the coreactant are slow to saturate.
  • SoftFast introduces a soft-saturating precursor, where after a fast rise it slowly saturates.
  • FastFast3 is a version of FastFast where the saturated growth per cycle is 0.3 Angstrom.

In addition to these environments, which are fully self-limited, aldenv also contains environments where the growth has a CVD component. For instance:

  • FastFastCVD01 has a built in CVD component of 0.1 Angstrom per second. This means that a 10 second dose give you an additional Angstrom due to the non self-limited behavior.

This results in the following saturation curve:

FastFast saturation curve