Pith Number

pith:Y3O4FLBN

pith:2026:Y3O4FLBNWA5FX745ZTVBD5TQQ3

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Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram

Amine Torki, Emmanuel Chanrion, Peter Samaha, Pierre-Andre Mortemousque, Sam Fiette, Yann Beilliard, Ysaline Renaud

A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success.

arxiv:2604.13662 v1 · 2026-04-15 · cond-mat.mes-hall · cs.CV · cs.LG

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Claims

C1strongest claim

Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs.

C2weakest assumption

The manually annotated dataset of 1015 CSDs from nine geometries is representative of future devices and that successful segmentation of transition lines directly corresponds to correct physical identification of the single-electron regime without systematic false positives on unseen wafers.

C3one line summary

A U-Net CNN segments experimental charge stability diagrams to locate the single-charge regime in 300 mm FDSOI quantum dots with 80% overall success and up to 88% on some designs.

References

57 extracted · 57 resolved · 2 Pith anchors

[1] Data Acquisition Energy Cryostat Automatic detection of 1e- regime using stability diagram segmentation Trained U-Net model Predicted maskStability diagram real-time data flow offline data flow Datase

[2] Inference pre-processing post-processing 1e- regime MobileNetV2 custom decoder SET Qubit FIG. 1. Schematic summary of the offline auto-tuning pipeline. T op (Data acquisition): experimental setup and

[3] Single QD-SET

[4] Model training2. Data annotation Normalize Load annotated dataset Evaluate performance Obtain dataset of 1015 labeled samples Perform 5-fold training Load stored CSDs Filter out irrelevant* CSDs Annot

[5] Inference Load one CSD Trained U-Net model Threshold & binarize Skeletonize Normalize Compute region centroid Output gate voltages (VQD, VSET) Extract region between first two transition lines Morphol

Receipt and verification

First computed	2026-06-19T16:10:37.440845Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5

Aliases

arxiv: 2604.13662 · arxiv_version: 2604.13662v1 · doi: 10.48550/arxiv.2604.13662 · pith_short_12: Y3O4FLBNWA5F · pith_short_16: Y3O4FLBNWA5FX745 · pith_short_8: Y3O4FLBN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6dde6be0ef8571dda6ec0667f0f8b527f6b1aa402cbc5c48b906ec1887766d94",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cond-mat.mes-hall",
    "submitted_at": "2026-04-15T09:29:24Z",
    "title_canon_sha256": "1aae7d822c97c30feee94e3a99f4fcf316fa2378b7f837cb412c9ac4f1c1ebf0"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.13662",
    "kind": "arxiv",
    "version": 1
  }
}