dsa-process-controller

Directed Self-Assembly skill for block copolymer lithography and nanoparticle templating

509 stars

Best use case

dsa-process-controller is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Directed Self-Assembly skill for block copolymer lithography and nanoparticle templating

Teams using dsa-process-controller should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/dsa-process-controller/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/nanotechnology/skills/dsa-process-controller/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/dsa-process-controller/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How dsa-process-controller Compares

Feature / Agentdsa-process-controllerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Directed Self-Assembly skill for block copolymer lithography and nanoparticle templating

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# DSA Process Controller

## Purpose

The DSA Process Controller skill provides directed self-assembly process control for block copolymer lithography and nanoparticle templating, enabling sub-lithographic patterning through controlled polymer phase separation.

## Capabilities

- Block copolymer selection and design
- Annealing protocol optimization
- Defect density analysis
- Pattern transfer protocols
- Graphoepitaxy and chemoepitaxy
- Long-range order characterization

## Usage Guidelines

### DSA Process Control

1. **BCP Selection**
   - Match pitch to target
   - Consider chi-N product
   - Select morphology (lamellar, cylindrical)

2. **Annealing Optimization**
   - Choose thermal vs solvent vapor
   - Optimize temperature/time
   - Achieve equilibrium morphology

3. **Defect Analysis**
   - Classify defect types
   - Quantify defect density
   - Identify root causes

## Process Integration

- Directed Self-Assembly Process Development
- Nanolithography Process Development

## Input Schema

```json
{
  "bcp_system": "string (e.g., PS-b-PMMA)",
  "target_pitch": "number (nm)",
  "morphology": "lamellar|cylindrical|spherical",
  "guiding_type": "graphoepitaxy|chemoepitaxy",
  "substrate_pattern": "string"
}
```

## Output Schema

```json
{
  "annealing_protocol": {
    "method": "thermal|svA",
    "temperature": "number (C)",
    "time": "number (hours)",
    "solvent": "string (optional)"
  },
  "achieved_pitch": "number (nm)",
  "defect_density": "number (defects/um2)",
  "correlation_length": "number (nm)",
  "pattern_quality": "string"
}
```

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