{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RYBKGTI6TMLJDVKT4GO42ATCBF","short_pith_number":"pith:RYBKGTI6","schema_version":"1.0","canonical_sha256":"8e02a34d1e9b1691d553e19dcd0262095052dae408c7ad93f092423ea7154772","source":{"kind":"arxiv","id":"2304.14408","version":3},"attestation_state":"computed","paper":{"title":"Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexander E. Siemenn, Armi Tiihonen, Basita Das, Eunice Aissi, Fang Sheng, Hamide Kavak, Tonio Buonassisi","submitted_at":"2023-03-16T17:30:51Z","abstract_excerpt":"High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to $10^4$ samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of $10^1$ samples"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.14408","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-03-16T17:30:51Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2a719f303b86fea0488deb998a97407e7594414532f48fcbd27a23a6c667bbb3","abstract_canon_sha256":"7fd23e851af86a3b25ce15d037cc1c72c73632dc3cc5f147e049dbc36a88febe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:31:39.914574Z","signature_b64":"nHiR9hG9Kmh+sw/bf5EnMj1jzJN9ViIM+2y7wvqGlr7ZpCeyv02Ad5rrGZlHtHd4wei+NAaP6yiBpIV4/NcdAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e02a34d1e9b1691d553e19dcd0262095052dae408c7ad93f092423ea7154772","last_reissued_at":"2026-07-05T08:31:39.914147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:31:39.914147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexander E. Siemenn, Armi Tiihonen, Basita Das, Eunice Aissi, Fang Sheng, Hamide Kavak, Tonio Buonassisi","submitted_at":"2023-03-16T17:30:51Z","abstract_excerpt":"High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to $10^4$ samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of $10^1$ samples"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.14408","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.14408/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.14408","created_at":"2026-07-05T08:31:39.914206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.14408v3","created_at":"2026-07-05T08:31:39.914206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.14408","created_at":"2026-07-05T08:31:39.914206+00:00"},{"alias_kind":"pith_short_12","alias_value":"RYBKGTI6TMLJ","created_at":"2026-07-05T08:31:39.914206+00:00"},{"alias_kind":"pith_short_16","alias_value":"RYBKGTI6TMLJDVKT","created_at":"2026-07-05T08:31:39.914206+00:00"},{"alias_kind":"pith_short_8","alias_value":"RYBKGTI6","created_at":"2026-07-05T08:31:39.914206+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF","json":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF.json","graph_json":"https://pith.science/api/pith-number/RYBKGTI6TMLJDVKT4GO42ATCBF/graph.json","events_json":"https://pith.science/api/pith-number/RYBKGTI6TMLJDVKT4GO42ATCBF/events.json","paper":"https://pith.science/paper/RYBKGTI6"},"agent_actions":{"view_html":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF","download_json":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF.json","view_paper":"https://pith.science/paper/RYBKGTI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.14408&json=true","fetch_graph":"https://pith.science/api/pith-number/RYBKGTI6TMLJDVKT4GO42ATCBF/graph.json","fetch_events":"https://pith.science/api/pith-number/RYBKGTI6TMLJDVKT4GO42ATCBF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF/action/storage_attestation","attest_author":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF/action/author_attestation","sign_citation":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF/action/citation_signature","submit_replication":"https://pith.science/pith/RYBKGTI6TMLJDVKT4GO42ATCBF/action/replication_record"}},"created_at":"2026-07-05T08:31:39.914206+00:00","updated_at":"2026-07-05T08:31:39.914206+00:00"}