Integrity report for An Unsupervised Machine Learning-based Framework for Wafer Scale Variability Analysis and Performance Prediction of Ferroelectric Hf0.5Zr0.5O2 Thin Film Capacitors

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.00544

0Critical

0Advisory

2Detectors run

2026-05-20Last checked

Paper page arXiv integrity.json

Detector runs

ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-20 19:40:11.328863+00:00

doi_compliance completed v1.0.0 · findings 0 · 2026-05-19 18:04:11.413702+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/2605.00544/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.