Electron Beam Lithography skill for high-resolution nanopatterning with dose optimization and proximity effect correction
Scanned 9/2/2026
Install to Claude Code
npx -y skills add a5c-ai/babysitter --skill ebl-process-controller --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: ebl-process-controller
description: Electron Beam Lithography skill for high-resolution nanopatterning with dose optimization and proximity effect correction
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: fabrication
priority: high
phase: 6
tools-libraries:
- BEAMER
- GenISys TRACER
- Raith NanoSuite
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]
---
# EBL Process Controller
## Purpose
The EBL Process Controller skill provides comprehensive electron beam lithography process control, enabling high-resolution nanopatterning through dose optimization, proximity effect correction, and critical dimension control.
## Capabilities
- Pattern design and fracturing
- Dose optimization and modulation
- Proximity effect correction (PEC)
- Alignment and overlay control
- Resist processing optimization
- Critical dimension (CD) control
## Usage Guidelines
### EBL Process Control
1. **Pattern Preparation**
- Design in CAD software
- Fracture into write fields
- Apply beam step size
2. **Dose Optimization**
- Run dose matrices
- Apply PEC algorithms
- Account for pattern density
3. **Process Integration**
- Optimize resist thickness
- Control development conditions
- Verify feature dimensions
## Process Integration
- Nanolithography Process Development
- Nanodevice Integration Process Flow
## Input Schema
```json
{
"pattern_file": "string",
"resist": "string",
"thickness": "number (nm)",
"target_cd": "number (nm)",
"beam_voltage": "number (kV)",
"beam_current": "number (pA)"
}
```
## Output Schema
```json
{
"optimized_dose": "number (uC/cm2)",
"pec_parameters": {
"alpha": "number",
"beta": "number",
"eta": "number"
},
"write_time": "number (hours)",
"expected_cd": "number (nm)",
"cd_uniformity": "number (3sigma)"
}
```
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