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nanoimprint-process-controller skill

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This skill helps you optimize nanoimprint lithography processes by managing templates, controlling temperature and pressure, and analyzing defects for

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---
name: nanoimprint-process-controller
description: Nanoimprint Lithography skill for high-throughput nanopatterning with template management and demolding optimization
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: nanotechnology
  domain: science
  category: fabrication
  priority: medium
  phase: 6
  tools-libraries:
    - NIL process simulation
    - Template design tools
---

# Nanoimprint Process Controller

## 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

1. **Template Preparation**
   - Design with demolding in mind
   - Apply anti-sticking treatment
   - Verify pattern fidelity

2. **Imprint Optimization**
   - Optimize pressure and temperature
   - Control residual layer thickness
   - Minimize defects

3. **Yield Improvement**
   - Track defect types
   - Optimize demolding conditions
   - Implement cleaning protocols

## Process Integration

- Nanolithography Process Development
- Directed Self-Assembly Process Development

## Input Schema

```json
{
  "template_id": "string",
  "resist_type": "thermal|uv_curable",
  "target_features": {
    "min_cd": "number (nm)",
    "pitch": "number (nm)",
    "aspect_ratio": "number"
  },
  "substrate": "string"
}
```

## Output Schema

```json
{
  "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 (%)"
}
```

Overview

This skill provides automated control for nanoimprint lithography workflows focused on high-throughput nanopatterning. It combines template management, imprint parameter optimization, and demolding analysis to maximize yield and minimize defects. The skill outputs actionable process parameters and key metrics for integration with production systems.

How this skill works

The controller takes template and substrate inputs plus target feature specs, then evaluates template readiness and recommends anti-sticking treatments. It runs an optimization loop over temperature, pressure, time and UV dose (for UV-NIL) to reach target residual layer and pattern fidelity. Finally it predicts demolding force, estimates defect density, and computes expected yield for use in process decisions.

When to use it

When scaling NIL from development to high-throughput production
When optimizing imprint pressure, temperature, and UV dose for new resist formulations
When evaluating template designs for demolding risk and anti-sticking requirements
When tracking defect types and prioritizing yield improvement actions
When integrating NIL with downstream directed self-assembly or lithography steps

Best practices

Design templates with demolding geometry and anti-sticking layers in mind
Run a parameter sweep across pressure/temperature before finalizing recipes
Use measured residual layer targets to calibrate imprint time and pressure
Log defect types with spatial metadata to identify systematic errors
Validate demolding force predictions with periodic pull tests on representative wafers

Example use cases

Generate optimized process parameters for thermal-NIL on silicon substrates with 50 nm minimum feature size
Recommend UV dose and exposure timing for UV-curable resists to minimize residual layer
Predict demolding force for a new template design to guide anti-sticking treatment selection
Assess defect density trends after a tool maintenance event and suggest corrective process changes
Produce a yield forecast for a batch run to inform production scheduling

FAQ

What inputs are required to generate a process recipe?

Provide template_id, resist_type, target_features (min CD, pitch, aspect ratio) and substrate. The skill uses these to evaluate template readiness and compute parameters.

Does it support both UV-NIL and thermal NIL?

Yes. The skill optimizes UV dose and exposure timing for UV-curable resists and temperature profiles for thermal processes.