Yield loss found too late
Defect patterns are identified after lots have already moved on, and root-cause analysis relies on manual correlation across tools.
Industries
Semiconductors & Hi-Tech
Yield analytics, visual inspection, test data platforms and engineering tools for chip and hardware companies.
Talk about your sector
Overview
Semiconductor and hardware companies generate some of the richest engineering data anywhere: wafer maps, test logs, equipment telemetry, inspection images and design files. Most of it is used once and archived. The teams that turn it into yield, throughput and time-to-market gains are the ones that treat data and AI as engineering disciplines, not experiments.
Arixent works with semiconductor manufacturers, fabless companies, equipment makers and hardware product teams on exactly that: analytics platforms for test and yield data, computer vision for inspection, engineering tools that speed up design and validation, and the product engineering behind the software these companies sell alongside their hardware.
Working on this in your sector? Let's compare notes.
Challenges
What gets in the way
Test data everywhere, insight nowhere
Terabytes of ATE, parametric and burn-in data sit in silos with no shared model or governed access.
Visual inspection limits
Rule-based optical inspection misses subtle defects and floods engineers with false alarms.
Engineering tool sprawl
Internal tools for design review, validation and lab management are aging and hard to extend.
Global teams, fragmented workflows
Design centers in the US, India and Asia work across systems that do not talk to each other.
Export control and IP sensitivity
Any partner must work inside strict security, data residency and access constraints.
Bring the use case. We will bring the engineering point of view.
Solutions
What we build
Yield and test analytics platforms
Governed data platforms and analytics for ATE, parametric and wafer-level data with correlation and root-cause tooling.
Explore this serviceComputer vision for inspection
Defect detection and classification on wafer, die and package images with human review for low-confidence cases.
Explore this servicePredictive models for yield and equipment
Excursion prediction, equipment health and process drift models integrated with manufacturing execution systems.
Explore this serviceEngineering tools and internal platforms
Custom software for design collaboration, validation tracking, lab management and knowledge search.
Explore this serviceLLM assistants for engineering knowledge
Retrieval-based assistants over specifications, failure analysis reports and process documentation.
Explore this serviceDedicated engineering teams across three time zones
Extended teams and capability centers for software, data and AI work supporting global design and manufacturing sites.
Explore this serviceWhere it fits
Typical use cases
- Wafer map pattern recognition and automatic classification
- Test time reduction through adaptive test analytics
- Equipment telemetry pipelines and predictive maintenance
- Failure analysis knowledge assistants
- Supply and demand planning for components
- Customer-facing software and portals for hardware products
FAQ
Questions about Semiconductors & Hi-Tech
Related services
Capabilities that apply here
Next step
Ready when you are.
Tell us what you are trying to build and we will come back with a point of view, not a pitch.