{"paper":{"title":"Characterization and Predictive Modeling of Epitaxial Silicon-Germanium Thermistor Layers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mes-hall","quant-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"B. Gunnar Malm, Frank Niklaus, Fredrik Forsberg, Mohammadreza Kolahdouz","submitted_at":"2011-12-09T08:31:49Z","abstract_excerpt":"The thermal coefficient of resistance (TCR) for epitaxial silicon-germanium (SiGe) layers has been analyzed by experiment and simulation. Predictive simulation using drift-diffusion formalism and self-consistent quantum-mechanical solutions yielded similar results, TCR around 2%/K at 300 K. This modeling approach can be used for different, graded and constant, SiGe profiles,. It is also capable of predicting the influence of background auto-doping on the TCR of the detectors"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1112.2043","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}