Dev
Scanning Tunneling Microscopy (STM) — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| tip_electronic_structure | -0.15 – 0.15 | - |
| piezo_creep | -1.2 – 1.8 | - |
| tunneling_barrier_height | 4.14 – 5.04 | eV |
| vibration_amplitude | -1.2 – 1.8 | pm |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SPM-Former + gradient | 0.723 | 28.93 | 0.899 | 0.87 | ✓ Certified | Chen et al., NanoLett 2024 |
| 2 | DeepSPM + gradient | 0.644 | 25.24 | 0.81 | 0.84 | ✓ Certified | Alldritt et al., Commun. Phys. 2020 |
| 3 | E2E-BTR + gradient | 0.641 | 25.62 | 0.821 | 0.78 | ✓ Certified | Kossler et al., Sci. Rep. 2022 |
| 4 | ScoreSPM + gradient | 0.613 | 24.13 | 0.773 | 0.81 | ✓ Certified | Wei et al., 2025 |
| 5 | U-Net-SPM + gradient | 0.583 | 22.69 | 0.719 | 0.84 | ✓ Certified | SPM U-Net variant |
| 6 | Reg-Deconv + gradient | 0.579 | 22.33 | 0.704 | 0.87 | ✓ Certified | Dongmo et al., 2000 |
| 7 | BTR + gradient | 0.534 | 20.83 | 0.638 | 0.85 | ✓ Certified | Villarrubia, JRNIST 1997 |
| 8 | TV-Deconvolution + gradient | 0.530 | 21.07 | 0.649 | 0.8 | ✓ Certified | TV regularization for SPM |
| 9 | MLE Reconstruction + gradient | 0.523 | 20.74 | 0.634 | 0.81 | ✓ Certified | Classical statistical method |
| 10 | DiffusionSPM + gradient | 0.465 | 18.9 | 0.545 | 0.79 | ✓ Certified | Zhang et al., 2024 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%