Trusted by the industry. Proven results.

WAT and PCM analysis

Analyze parameters measured during Wafer Acceptance Test (WAT) and Process Control Monitor (PCM) to understand upstream process variation during ramp.

  • Analyze parametric performance across wafers and lots

  • Compare site-to-site test performance across testers

  • Identify fab process variation affecting downstream test yield

  • Correlate WAT and PCM parameters with wafer probe and final test data

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Wafer sort analysis

Visualize wafer performance across parametric data, bin distributions, and yield during early production.

  • Wafer map visualization across parametric and bin data

  • Stack and analyze hundreds of wafers simultaneously

  • Detect spatial yield patterns and systematic variation

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Site-to-site performance comparison

Identify measurement variation and yield loss across probe sites and test systems during ramp.

  • Compare site performance across wafers and lots

  • Detect recurring site-related yield loss

  • Search historical wafers affected by site variation

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Detect test program changes

Identify unintended test program revisions that may affect yield during ramp.

  • Detect program changes even when program names remain unchanged

  • Compare limits, revisions, and added or removed tests

  • Validate program consistency across testers and sites

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ANOVA yield analysis

Determine which factors are driving yield and parametric variation during ramp-to-volume.

  • Apply ANOVA across wafers, lots, sites, and test conditions

  • Identify variables impacting yield performance

  • Detect interactions between parameters and operating conditions

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CP1 / CP2 wafer sort analysis

Analyze multi-pass wafer sort testing performed under different conditions such as ambient and hot.

  • Compare CP1 and CP2 performance across wafers and lots

  • Detect temperature-dependent yield behaviour

  • Consolidate multi-pass wafer sort datasets for analysis

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Gage R&R analysis

Validate measurement repeatability across testers, sites, and probe setups during ramp.

  • Identify tests sensitive to setup variation

  • Verify measurement repeatability across test environments

  • Share Gage R&R results with OSAT and test partners

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Rescreen and retest analysis

Monitor retest and rescreen activity across manufacturing partners during ramp.

  • Access raw and rescreened datalogs

  • Analyze retest behavior across wafers and lots

  • Validate adherence to agreed rescreen plans

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Cross-stage correlation

Correlate fab, wafer probe, and final test data to understand how upstream variation impacts downstream device performance.

  • Link WAT, CP, and FT results in a unified dataset

  • Trace device performance from fabrication through packaged test

  • Identify early indicators of yield loss during ramp

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Supply chain collaboration

Enable shared analysis across fabless companies, foundries, and test houses during production ramp.

  • Secure data sharing with OSATs and foundries

  • Analyze production data across manufacturing partners

  • Accelerate root cause investigation across the supply chain

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Transition from ramp to stable production

Once yield stabilizes, engineering focus shifts toward sustaining yield performance and optimizing cost per unit across global production.

Explore production

Chosen by top semiconductor companies

“ Days of effort that were required before we had yieldHUB have now been eliminated."

EdgeQ

Data extraction is a breeze

“ I am happy with the speed, and the amount of data yieldHUB is capable of managing."

Ambiq

Speed & scalability

“ Our test yield has become better through proactive Fab improvement and shorter feedback time from the Fab and test house."

TDK

Faster fab improvement

“ The charts and graphs make it easy to identify problems and their causes. It makes my job easier. The team is happier and more productive."

Microchip

Diverse analysis

“ I like the system for its ability to be a one-stop shop for data review, statistical analysis, and reporting."

Infineon

With yieldHUB, we can identify and solve problems.

“ I am impressed by the remarkable speed yieldHUB processes large datasets"

Test Development Engineer, EnSilica

Masters the big data problem

“ yieldHUB’s system is very fast and processes vast amounts of data within minutes."

ADTechnology Inc

10 times more efficient

“ yieldHUB certainly masters the ‘big data’ problem."

Test Development Engineer, EnSilica

Fast big data processing & Cp/Cpk yield optimization

“ The best combination of production visibility and intuitive design."

VP Product & Test Engineering, Movandi

Production visibility, usability, & scalability

“ Their big data software and worldwide support has allowed us to manage our yields effectively."

VP Corporate Operations, Diodes

Big data scalability & responsive support

“ I am able to do extremely detailed analysis very quickly."

Principal Product Engineer, Xaar

Fast, detailed silicon variation analysis

“ yieldHUB makes my team 10 times more efficient."

Principal Engineer - Test Development, ADTechnology Inc

Quicker STDF processing time

“ It has the feel of being a customised tool."

VP Manufacturing & Quality, Decawave (Now Qorvo)

Flexible to support manufacturing challenges

“ With a simple click, it can take you all the way down to a single die."

Director of Product & Quality Engineering, Cambridge GaN Devices

Die-level correlation across stages

“ We were able to improve the yield by 4% and it’s now very stable."

Product Development Manager, EnSilica

Improved yield & stabilized ramp

“ I can identify problems in just one minute."

Principal Engineer - Test Development, ADTechnology Inc

Rapid root cause identification

“ yieldHUB plays a very important daily role in our PE and QE teams."

Director of Product & Quality Engineering, Cambridge GaN Devices

Used daily in PE & QE teams

“ You can log in from any web browser and do loads of data correlations."

Director of Product & Quality Engineering, Cambridge GaN Devices

Browser-based

“ I like the system for its ability to be a one-stop shop for data review, statistical analysis, and reporting."

Product Engineering Manager, Infineon

Consolidates data review, analysis, & reporting in one system

“ yieldHUB increased productivity 60 times in that case."

Product Engineering Manager, Infineon

Reduced analysis time from hours to minutes.

“ It’s much faster typically in yieldHUB."

Product Engineer, Movandi

Accelerates routine production yield investigations

“ With yieldHUB it’s easy to connect the chip data to the module data."

Product Engineer, Movandi

End-to-end traceability

“ We are extremely pleased with yieldHUB and its ability to cope with our high production volume."

Engineer, Raspberry Pi

Handles high-volume production data

“ Something that’s very helpful to us is the ability within yieldHUB to click and drill down, by test or by part."

Engineer, Raspberry Pi

Enables fast drill-down

“ It’s helped us to beat our turnaround time in development."

Test Engineer, Boréas Technologies

Improved development speed and time-to-market.

“ Your customer service has always been perfect."

Test Engineer, Boréas Technologies

Highly responsive support

“ It must be in one well-integrated system where you can run correlation at different stages."

Director of Product & Quality Engineering, Cambridge GaN Devices

Correlation across production stages

“ yieldHUB has been organized quite well and it’s easy to use."

Director of Product & Quality Engineering, Cambridge GaN Devices

User-friendly design

“ We need to drill down to the site level detail to see if the spec requirements are well met."

Director of Product & Quality Engineering, Cambridge GaN Devices

Enables detailed site-level performance validation before assembly.

“ In case of any fab issue, then you can contact the Foundry guys and share the data."

Director of Product & Quality Engineering, Cambridge GaN Devices

Foundry collaboration

“ yieldHUB enabled us to aggregate all collected data quickly and visualize the data in one place."

Senior Manager of Product Engineering, Ambiq

Multi-OSAT data consolidation

“ yieldHUB’s speed and scalability have been impressive."

Senior Manager of Product Engineering, Ambiq

Years of data, Die-level visibility

“ The genealogy correlation enables me to notice any issue as it happened."

Senior Manager of Product Engineering, Ambiq

End-to-end traceability

“ yieldHUB has made my job much easier by linking wafer fab data all the way to final test."

Senior Manager of Product Engineering, Ambiq

Fab-to-final-test linkage

“ Data extraction is a breeze."

Director of Product & Test Engineering, EdgeQ

Automated OSAT uploads

“ Days of effort that were required before we had yieldHUB have now been eliminated."

Director of Product & Test Engineering, EdgeQ

Manual data crunching removed

“ The platform is simple to use to extract data from any tests."

Director of Product & Test Engineering, EdgeQ

Statistical limits & graphical summaries

“ We have greatly exceeded our goals in a shorter time frame than expected."

VP of Quality, Integra Technologies

ROI exceeded & statistical yield limits deployed

“ We were able to reduce average cycle times by over 70%."

VP of Quality, Integra Technologies

70% cycle time reduction

“ Our test yield has become better through proactive Fab improvement."

Chief Project Engineer, TDK

SYL & SBL deployment

“ It absolutely saves us time"

Chief Project Engineer, TDK

Automated abnormality detection

“ yieldHUB coped with over 20 million units in our first year of production."

Engineer, Raspberry Pi

20M+ units & high-volume production

“ yieldHUB has made my job much easier by linking wafer fab data all the way to final test."

Senior Manager of Product Engineering, Ambiq

Fab-to-final-test genealogy correlation

“ yieldHUB allows you to upgrade as your product volume scales up."

Director of Product & Quality Engineering, Cambridge GaN Devices

Supports growth from NPI to high-volume production

“ Our test yield has become better through proactive Fab improvement."

Chief Project Engineer, TDK

Improved test yield through structured statistical limits and Fab collaboration

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