Atomic Layer Deposition skill for conformal thin film deposition with atomic-level thickness control
Scanned 9/2/2026
Install to Claude Code
npx -y skills add a5c-ai/babysitter --skill ald-process-controller --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Ald Process Controller?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/a5c-ai-ald-process-controller-babysitter)More formats (shields.io, HTML) on the badges page.
---
name: ald-process-controller
description: Atomic Layer Deposition skill for conformal thin film deposition with atomic-level thickness control
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: fabrication
priority: high
phase: 6
tools-libraries:
- ALD process simulators
- Quartz crystal microbalance analysis
graph:
domains: [domain:nanotechnology]
skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-engineer]
---
# ALD Process Controller
## Purpose
The ALD Process Controller skill provides comprehensive atomic layer deposition process control, enabling conformal thin film growth with atomic-level precision through optimized pulse sequences and in-situ monitoring.
## Capabilities
- Precursor pulse/purge optimization
- Growth per cycle (GPC) characterization
- Film uniformity mapping
- Conformality assessment
- In-situ monitoring integration
- Multi-component film design
## Usage Guidelines
### ALD Process Control
1. **Saturation Studies**
- Vary pulse times
- Identify saturation dose
- Optimize purge times
2. **Process Window**
- Determine ALD window
- Optimize temperature
- Monitor GPC stability
3. **Film Quality**
- Characterize uniformity
- Measure conformality
- Assess impurity levels
## Process Integration
- Thin Film Deposition Process Optimization
- Nanodevice Integration Process Flow
## Input Schema
```json
{
"material": "string",
"precursor_a": "string",
"precursor_b": "string",
"target_thickness": "number (nm)",
"substrate": "string",
"temperature": "number (C)"
}
```
## Output Schema
```json
{
"optimized_recipe": {
"precursor_a_pulse": "number (s)",
"purge_a": "number (s)",
"precursor_b_pulse": "number (s)",
"purge_b": "number (s)"
},
"gpc": "number (Angstrom/cycle)",
"cycles_required": "number",
"uniformity": "number (%)",
"conformality": "number (% step coverage)"
}
```
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!