Automation and AI that earn their place on the line.
What to automate, which AI use cases pay first, and how to run them, from people who have worked inside fully automated 300 mm fabs and led AI-enabled manufacturing programmes.
- Delivered by
- FabBeyond; builds with named partners
- For
- CEOs, CTOs, fab and plant heads, IT and OT leaders
- Covers
- Equipment automation, material handling, MES, data, AI
- Starts with
- An automation and data readiness assessment
01 The automation stack
AI sits on top of five floors of automation.
A model is only as good as the data under it. We look at every level, from the tool’s sensors to the people who act on a prediction.
Decisions and people
Dashboards, shift reviews, AI assistants that answer a technician’s question from your own procedures.
We helpDesign the decisions before the dashboards.
Analytics and AI
Yield and bin-map analytics, predictive maintenance, virtual metrology, vision inspection.
We helpPick the use cases that pay, and prove them.
Manufacturing execution
MES, dispatching, recipe management, statistical process control, run-to-run control.
We helpSpecify, select and govern the rollout.
Material handling
Overhead transport, stockers, FOUP and magazine logistics, dispatch rules.
We helpSize it to the ramp, not to the brochure.
Equipment automation
Tool connectivity to the host, recipe download, alarms and data collection.
We helpReadiness checks, tool by tool, before go-live.
Tools and sensors
Process tools, sensors and facility systems: where the data begins.
We helpFind the gaps in what is measured.
02 Where AI pays first
Six use cases with a short path to value.
Each one is narrow, measurable and built on data a running line already collects.
Predictive maintenance
Spot a pump, chamber or robot heading for failure, and service it in a planned window.
Needs: tool sensor and maintenance history.
Fault detection and classification
Flag an abnormal run while it is happening, not after the lot is scrapped.
Needs: run-level trace data.
Yield and bin-map analytics
Group wafer-map signatures and trace them to the tools and lots they share.
Needs: sort results and lot history.
Visual inspection
Vision models for die, bond and package defects in assembly and test.
Needs: labelled images from your line.
Virtual metrology
Estimate a measurement from tool data, so fewer wafers wait for the metrology tool.
Needs: paired tool and metrology data.
Technician assistant
Answers from your own procedures and fault history, on the floor, on every shift.
Needs: your documents, kept current.
03 Automation maturity
Five levels. Most new lines should start at the third.
Build automated from day one. Predictive and self-correcting are earned with data, one tool at a time.
- 01ManualPeople move wafers and record data by hand.
- 02ConnectedTools report status, alarms and data to the host.
- 03AutomatedMaterial handling, dispatch and recipes run without hands.
- 04PredictiveModels flag drift, faults and maintenance before they bite.
- 05Self-correctingRun-to-run control and AI adjust the process within limits.
04 How we engage
Assess, pilot, prove, then scale.
- 01AssessAutomation and data readiness, tool by tool.
- 02PrioritiseUse cases ranked by value, effort and data in hand.
- 03PilotOne tool, one use case, one measured result.
- 04ScaleRoll out with integration and data partners.
- 05SustainModels monitored; your people trained to own them.
Our own people
- Automation strategy and roadmapWhat to automate, in what order, tied to the ramp.
- Automation and data readiness assessmentTool by tool, level by level.
- Use-case selection and business caseRanked by value, effort and data in hand.
- Vendor and partner selectionShortlists with written reasons.
- Pilot governance and value trackingOne measured result before anything scales.
- Workforce upskilling planSo your team owns the systems after go-live.
With a named partner
- MES and material-handling implementationBy the system vendor or integrator.
- Equipment connectivity integrationBy automation integration partners.
- Data platforms and model buildingBy data-engineering partners.
- Vision inspection systemsBy machine-vision specialists.
05 Why us
Advice from people who have run automated lines.
Our leadership has led teams inside a fully automated 300 mm fab, delivered fab-automation services, and directed AI-enabled manufacturing programmes. We know where automation saves a ramp and where it only adds cost.
- Automated 300 mm fabTeams led inside one
- Fab automation servicesDelivered in India
- AI-enabled manufacturingProgrammes directed
- 1,400+ installsTools, integrations, readiness
- Up to 450 engineersLed at peak, for global OEMs
- Vendor-neutralWe sell no software or tools
06 Questions
AI and automation, answered plainly.
What does AI and automation consulting for a semiconductor fab cover?
Automation strategy and roadmaps, readiness assessments, equipment connectivity, material handling and manufacturing execution advisory, and AI use cases such as predictive maintenance, fault detection, yield analytics, virtual metrology and visual inspection. FabBeyond advises and governs; named partners build and integrate.
Which AI use cases pay first in a fab or OSAT?
Narrow ones built on data the line already collects: predictive maintenance on critical tools, fault detection on high-value steps, yield and bin-map analytics, and vision inspection in assembly and test. Each should be piloted on one tool and measured before it is scaled.
Do we need perfect data before starting with AI?
No, but you need the right data for one use case. A readiness assessment shows what each tool already records, what is missing, and which use case can start now.
Does FabBeyond build AI models or implement MES?
No. FabBeyond provides strategy, assessment, use-case selection, vendor and partner selection, pilot governance and workforce planning. Model building, data platforms, MES and material-handling implementation are delivered by named partners.
How automated should a new fab or OSAT line be on day one?
Most new lines should start fully automated in equipment connectivity, material handling and manufacturing execution, then earn predictive and self-correcting capabilities tool by tool as data accumulates.
Next step
What is your next semiconductor challenge?
Tell us what you are solving. We will tell you plainly whether we can help, and who would deliver it.