README.md

metadata

license: apache-2.0
base_model: mlx-community/Qwen3.5-4B-MLX-4bit
tags:
  - image-localization
  - template-matching
  - semiconductor
  - sem
  - siamese-network
  - lora
  - mlx
library_name: pytorch

Drift-Sense Localization

Models that find a zoomed-in SEM reference pattern (100x, 1 nm/px) inside a zoomed-out search image (10x, 10 nm/px) and return its pixel location. Trained on 400 synthetic FinFET SEM pairs (IRDS 2024 geometry) with realistic noise, blur, rotation (±2°), and stage drift (100–500 nm).

Files

file	what it is
cnn/best.pt	Siamese correlation CNN (PyTorch state_dict, ~6M params)
lora/adapters.safetensors	LoRA adapter (rank 8) for Qwen3.5-4B
lora/adapter_config.json	Adapter config for mlx-vlm

Results (held-out test split, 50 pairs)

model	mean err (px)	≤1 px	≤5 px	s/pair
Siamese CNN + sub-pixel refine	0.50	96%	98%	0.05
Siamese CNN (coarse)	2.51	10%	96%	0.04
Qwen3.5-4B LoRA	35.79	0%	0%	1.83

1 px = 10 nm. With phase-correlation refinement the CNN localizes to ~5 nm mean error. A blind nominal-spot baseline scores 34.9 px; the VLM does not beat it.

Usage

CNN (needs the training repo on PYTHONPATH):

import torch
from drift_sense.model import SiameseLocator
from drift_sense.predict import predict

out = predict("ref.png", "search.png", ckpt="best.pt")
# {'x': 273.1, 'y': 262.4, 'confidence': 0.77, 'found': True,
#  'message': 'Pattern found at (273, 262) in the search image. Confidence: 77%.'}

VLM adapter (with mlx-vlm):

python -m mlx_vlm.generate \
  --model mlx-community/Qwen3.5-4B-MLX-4bit \
  --adapter-path lora \
  --image ref.png search.png \
  --prompt "The first image is a zoomed-in reference pattern. The second image is a zoomed-out search image (1000x1000 pixels). Find the reference pattern in the search image. If several matches exist, pick the one closest to the centre. Answer exactly: Pattern found at (x, y). Confidence: high." \
  --max-tokens 40

Design notes

• The die is periodic (9 identical rail crossings), so pure correlation is ambiguous. The CNN adds a learnable position-prior bias map and trains with softmax cross-entropy over the full heatmap so peaks compete.
• Confidence = probability mass near the chosen peak; ambiguous matches score low. Ties break toward the image centre.
• The VLM run is an honest negative result: it learns the output format (0% parse failures) but not pixel-level precision.

Limitations

• Trained on synthetic SEM images only; not validated on real SEM data.
• Assumes drift within ~±50 px of the nominal position (as generated).
• Rotation handled implicitly up to ±2°; no rotation estimate is output.