Nexus Data Hive
Turn large sets of raw measurement data into pure insight (OR Organize your measurement data, from storage to extraction?). As part of the Nexus product line, Nexus Data Hive
ingests, structures, and manages
photonics measurement data, transforming complex measurements into clean, actionable information. By aligning your libraries with the fab’s physical reality, it helps you take control of your PIC data flow.
About 
Nexus is our product line designed for photonics data management and analytics, featuring Nexus Data Hive and Nexus Data Explorer. Nexus provides a standardized infrastructure for hardware-driven validation, helping you move away from fragmented scripts and manual data processing toward a fully integrated, automated data pipeline.
About Nexus Data Hive
Nexus Data Hive is a server-based photonics and microelectronics measurement data pipeline and management platform engineered for foundries, IDMs, pilot lines, and mid-to-large fabless teams. Built to manage large, fragmented on-automated workflows that eliminate reliance on fragile, unmanageable individual scripts. By bridging the gap between raw test instrumentation and structured databases, it automatically extracts test key designs and performance metrics directly from measurement data.
Data Hive drastically shortens the time required for data analysis, yield improvement, and model validation, effectively eliminating the bottlenecks that delay learning between tape-out runs. Available for secure, on-premises server deployment, it provides a high-performance environment where teams can standardize data processing tasks (such as parsing, de-embedding, and parameter extraction) using our powerful engine, which permits custom Python script add-ons.
Ultimately, Nexus Data Hive establishes a standardized infrastructure for silicon-driven validation, helping you transform massive silicon data into actionable insights to optimize both design and fab processes.
Core Features
- Enterprise On-premises Infrastructure: Deployed fully on local servers or private clouds with containerized scaling, guaranteeing absolute data sovereignty while handling high-concurrency ingestion and massive storage throughput.
- Automated Hierarchical Metadata Parsing: Automatically extracts and maps unstructured test headers into a standardized, searchable hierarchy: Lot → Wafer → Die → Test Key, with accurate execution timestamps.
- Algorithmic De-embedding: Features a workflow to systematically strip out parasitic and insertion losses introduced by optical fibers, grating couplers, and electrical probes, using user-defined de-embedding scripts.
- Automated Photonics Parameter Extraction:
- Optical Metrics: Write scripts for curve fitting and extraction of optical loss, effective index, center wavelength, and quality factor.
- Electrical Metrics: Write scripts for parametric extraction of photodiode (PD) I–V curves, dark current, and breakdown voltage.
Benefits
- Reduce the time required to process and validate large-scale photonics measurement datasets from months to weeks through automation and standardized workflows, with the possibility of instant data feedback and interactivity.
- Standardize extracted measurement data for downstream statistical analysis and process monitoring workflows, and eliminate data reproducibility issues across different environments.
- Enable multi-team collaboration via reusable Python pipelines in a secure, on-premises environment. A centralized infrastructure eliminates local diskspace limits.
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