FlakeFinder

Automated 2D material flake detection system for the Sharpe Lab's Leica DM6M microscope. Scans silicon wafers, detects flakes (hBN, graphene, WSe₂), and uploads results to flakes.sharpelab.science.

Pipeline

The find-flakes command orchestrates the full scan-to-upload pipeline:

1. Overview scan (2.5x) — continuous-motion snake scan of the full stage area
2. Stitch + chip detection — assemble frames into panoramic image, find chip boundaries via Otsu thresholding
3. Per-chip focus map — autofocus at grid points, fit tilt plane for Z tracking
4. Chip scan (10x or 20x) — continuous-motion scan with real-time Z tracking along the focus plane
5. Segmentation — per-frame flake detection, classification, and tiered scoring (runs in background)
6. Revisit (50x) — re-image top-ranked flakes at higher magnification
7. Upload — package results and POST to flakes.sharpelab.science

Two scan presets:

Preset	Overview	Chip Scan	Speed	Use Case
2.5_10	2.5x	10x	10 mm/s	Fast screening
2.5_20	2.5x	20x	5 mm/s	Higher resolution

Supports checkpointing — re-run with --resume <run_dir> to pick up where a previous run left off.

See docs/architecture.md for detailed system design and hardware specs.

Material Presets

Segmentation is configured per-material via DetectorConfig presets. Each preset defines contrast thresholds, calibration curves (R/G contrast → thickness), classification gates, and scoring functions.

Preset	Material	Substrate	Calibration	Notes
hbn_medium	hBN	90nm SiO₂	AFM-verified	Primary hBN preset
hbn_thick_90nm	hBN	90nm SiO₂	AFM-verified	30-40nm hBN
hbn_medium_285nm	hBN	285nm SiO₂	Transfer matrix model	Tighter R gate for tape rejection
graphene_thin_90nm	Graphene	90nm SiO₂	Per-layer contrast	Layer counting (0.335 nm/layer)
graphene_thick_90nm	Graphene	90nm SiO₂	Per-layer contrast	Thick graphene (~5-10 nm); cal TBD
wse2_monolayer_90nm	WSe₂	90nm SiO₂	Point calibration	Single-layer reference

Detections are classified by proximity to the calibration curve (thin/medium/thick) and assigned a tier (T1 = high confidence, T2 = possible, T3 = unlikely) plus a continuous score based on size, shape, and calibration distance.

Installation

Requirements: Python 3.13+, Windows (for Leica SDK), uv package manager.

git clone git@github.com:sharpelab/flakefinder.git
cd flakefinder
uv sync
git config core.hooksPath hooks/

Leica SDK DLLs must be in src/flakefinder/dlls/ (already in place on the microscope PC). See docs/setup.md for details.

Usage

On the microscope PC

# Full pipeline with default preset (2.5x overview + 10x chip scan)
uv run find-flakes

# Higher resolution preset
uv run find-flakes --preset 2.5_20

# Graphene detection
uv run find-flakes --material graphene_thin_90nm

# Only scan specific chips
uv run find-flakes --chips 0,2,5

# Resume a previous run
uv run find-flakes --resume scans/run_20260301_1430

# Preview without running
uv run find-flakes --dry-run

Remotely via sls

The sls tool runs commands on the microscope PC over SSH:

sls find-flakes --dry-run          # run a command
sls stage                          # check stage position
sls pull scans/run_20260301_1430/  # download scan data
sls push calibration/flatfield.npy # upload a file
sls git status                     # run git on the microscope

GUIs

Command	Framework	Purpose
uv run find-flakes-gui	Tkinter	Form-based launcher for find-flakes (for collaborators)
uv run run-viewer	Tkinter	Browse completed runs, view detections with filters
uv run quick-scan	PySide6	Interactive stage viewer with live camera feed

Commands

All commands are registered as pyproject.toml entry points. Run with uv run <command> on the microscope or sls <command> remotely.

Command	Purpose
find-flakes	Full pipeline orchestrator
scan	Multi-row snake scan with continuous motion
stitch	Stitch scan frames into 2D overview image
find-chips	Detect chips in stitched image via Otsu thresholding
focus-map	Autofocus grid sampling across a chip
analyze-focus-map	Analyze focus map, fit tilt plane
chip-scan	Chip scan with continuous Z tracking
autofocus	Single-point Z-scan autofocus
capture	Single image capture
revisit	Revisit stage points with autofocus and capture
stage	Stage position, objective, and lamp control
upload	Upload run results to flakes.sharpelab.science
run-viewer	GUI: browse completed runs
find-flakes-gui	GUI: form-based pipeline launcher
quick-scan	GUI: interactive stage viewer

Analysis Scripts

Post-processing and analysis tools in scripts/. Run with uv run python scripts/<script>.py.

Script	Purpose
process_overview.py	Overview post-processing (rsync + stitch + chip detection)
process_chip_scan.py	Chip scan analysis (rsync + segmentation)
segment_chip_scan.py	Run segmentation on a completed chip scan
segment_flakes.py	Segment individual frames
crop_mosaic.py	Build detection mosaic grids with filtering (--tier, --where, --top)
eval_detections.py	Evaluate detection quality across runs
rerank_detections.py	Re-score detections with updated config
build_flatfield.py	Build flatfield calibration from blank frames
analyze_chip_scan.py	Scan quality analysis (Z tracking, frame pacing)
analyze_autofocus.py	Autofocus quality analysis
hbn_contrast.py	Transfer matrix hBN contrast model
hbn_contrast_widget.py	Interactive R/G contrast explorer with sliders
download_flakes.py	Download flake images from flakes.sharpelab.science

Tools

Tool	Purpose
tools/sls	Run commands on the microscope via SSH. Setup: ln -sf $(pwd)/tools/sls ~/.local/bin/sls
tools/scan-nb	Append timestamped entries to scan notebooks. Setup: ln -sf $(pwd)/tools/scan-nb ~/.local/bin/scan-nb

Directory Structure

├── src/flakefinder/
│   ├── leica/              # Hardware library: Stage, Camera, ZDrive, Lamp, etc.
│   ├── commands/           # CLI entry points (see Commands table)
│   ├── segmentation.py     # Flake detection: presets, scoring, classification
│   ├── scan_utils.py       # Geometry, interpolation, flatfield correction
│   ├── flakes_api.py       # Client for flakes.sharpelab.science
│   ├── cli_utils.py        # Argparse helpers, metadata builders
│   ├── data_utils.py       # Scan data loading
│   └── types.py            # NamedTuples (ScanMeta, FrameMeta, etc.)
├── src/quick_scan/         # PySide6 stage viewer GUI
├── scripts/                # Analysis and post-processing scripts
│   └── experiments/        # Hardware characterization experiments
├── tools/                  # sls, scan-nb, subtask-launch
├── docs/                   # Architecture, scan format, microscope reference, setup
├── calibration/            # Flatfield calibration images
├── scans/                  # Scan output data (gitignored)
└── archive/                # Superseded scripts kept for reference

Development

uv sync                                                  # install all deps
uv run ruff check --fix . && uv run ruff format .        # lint + format
uv run ty check                                          # type check
uv run pytest                                            # tests

Pre-commit hooks run ruff and ty automatically. Enable with git config core.hooksPath hooks/.

Links

• Production Site
• Wiki: FlakeFinder
• Original System
• Website Repo