Nanoimprint Process Controller
SkillDev toolsNanoimprint Lithography skill for high-throughput nanopatterning with template management and demolding optimization
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Nanoimprint Process Controller skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/nanotechnology/skills/nanoimprint-process-controller/SKILL.md and read by ahel’s review.
Purpose
The Nanoimprint Process Controller skill provides comprehensive nanoimprint lithography process control, enabling high-throughput nanopatterning through template design, imprint optimization, and defect management.
Capabilities
- Template design and fabrication
- Imprint pressure and temperature optimization
- UV-NIL and thermal NIL protocols
- Demolding force analysis
- Residual layer control
- Defect inspection and yield analysis
Usage Guidelines
NIL Process Control
-
Template Preparation
- Design with demolding in mind
- Apply anti-sticking treatment
- Verify pattern fidelity
-
Imprint Optimization
- Optimize pressure and temperature
- Control residual layer thickness
- Minimize defects
-
Yield Improvement
- Track defect types
- Optimize demolding conditions
- Implement cleaning protocols
Process Integration
- Nanolithography Process Development
- Directed Self-Assembly Process Development
Input Schema
{
"template_id": "string",
"resist_type": "thermal|uv_curable",
"target_features": {
"min_cd": "number (nm)",
"pitch": "number (nm)",
"aspect_ratio": "number"
},
"substrate": "string"
}
Output Schema
{
"process_parameters": {
"temperature": "number (C)",
"pressure": "number (bar)",
"time": "number (s)",
"uv_dose": "number (mJ/cm2)"
},
"residual_layer": "number (nm)",
"demolding_force": "number (N)",
"defect_density": "number (defects/cm2)",
"yield": "number (%)"
}
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
nanoimprint-process-controller- Source
- github.com/a5c-ai/babysitter