Beat 01
The loss
Inspection at cell test generated far more calls than the quality team could disposition, so real excursions waited behind nuisance while panels kept being made.
Case Studies
Deployment shapes, measured honestly
These are illustrative deployment shapes built from discovery interviews and design-partner scoping. Numbers are targets, clearly labelled, not audited results.
- Illustrative
- Targets labelled
- Method stated
Deployment shapes across line types
Gen-8.6 OLED fabFlexible OLED module lineAutomotive display makermicroLED pilot lineAR/VR microdisplay groupLTPS array fab
Shape 01
Mura false-call reduction on a Gen-6 flexible OLED line
Beat 02
The scope
One inspection line. mura-and-defect in shadow mode for eight weeks, trained on the fab's own true-versus-false-call history.
Beat 03
The mechanism
Classification separates large-area non-uniformity, sub-pixel opens and shorts, particles and stains, and attaches the evidence behind every retained call.
Beat 04
The measure
False-call rate and escape rate together — suppression that costs escapes is not a win. Both are measured against the pre-shadow baseline. [TARGET]
# closed loop, one panel
perceive substrate → tft → emitter stack → sub-pixel
plan deposition · anneal · transfer · test/bin
act recipe write-back (SECS-GEM)
sense mura ΔL* · sub-pixel opens · particles
optimise yield · ramp · scrap
log immutable audit entry ✓ signed
retrain engineer correction → model
Shape 02
Deposition drift control on a rigid OLED line
Beat 01
The loss
Thickness and overlay drift accumulated across mother glass for hours before showing as visible non-uniformity, producing predictable rework every ramp.
Beat 02
The scope
One deposition tool set. deposit-and-pattern in advisory mode with twin validation before any recommended recipe change.
Beat 03
The mechanism
Chamber telemetry and metrology fused into a drift trajectory; corrections simulated in the twin, approved by process integration, written back over SECS-GEM.
Beat 04
The measure
Percentage of panels produced inside the uniformity window, and rework rate, against the baseline quarter. [TARGET]
# closed loop, one panel
perceive substrate → tft → emitter stack → sub-pixel
plan deposition · anneal · transfer · test/bin
act recipe write-back (SECS-GEM)
sense mura ΔL* · sub-pixel opens · particles
optimise yield · ramp · scrap
log immutable audit entry ✓ signed
retrain engineer correction → model
Shape 03
Transfer yield on a microLED pilot line
Beat 01
The loss
Mass-transfer losses at millions of emitters per panel turned every fractional yield point into repair cycles or scrapped backplanes.
Beat 02
The scope
One transfer and repair cell. microled-transfer in advisory, with repair prioritisation by recovered value.
Beat 03
The mechanism
Placement and bonding parameters learned against measured outcomes; repair queue ordered by value rather than arrival.
Beat 04
The measure
Transfer yield and repair cycles per panel, plus cost per good panel. [TARGET]
# closed loop, one panel
perceive substrate → tft → emitter stack → sub-pixel
plan deposition · anneal · transfer · test/bin
act recipe write-back (SECS-GEM)
sense mura ΔL* · sub-pixel opens · particles
optimise yield · ramp · scrap
log immutable audit entry ✓ signed
retrain engineer correction → model
Method
How we would measure it on your line
The method matters more than the number. This is the one we would sign up to.
Step 1 of 6 · define
Define the metric
One metric, already reported, with an owner.
Step 2 of 6 · baseline
Measure the baseline
A full production period before any write-back.
Step 3 of 6 · shadow
Run shadow
Predictions logged and scored against your dispositions.
Step 4 of 6 · assist
Move to advisory
Engineers approve; overrides captured with reasoning.
Step 5 of 6 · compare
Compare like for like
Same products, same shifts, same criteria.
Step 6 of 6 · publish
Publish with consent
Nothing leaves your fab without written approval.
At a glance
The three shapes
All figures are design-partner targets, not audited outcomes.
| Shape | Line type | Agent | Primary metric |
|---|---|---|---|
| Mura false-call reduction | Gen-6 flexible OLED | mura-and-defect | False-call rate / escape rate |
| Deposition drift control | Rigid OLED | deposit-and-pattern | In-window rate / rework |
| Transfer yield | microLED pilot | microled-transfer | Transfer yield / repair cycles |
| Ramp acceleration | Automotive display | yield-and-ramp | Weeks to target yield |
What we will not do
No invented outcomes
It is easy to publish a 40% improvement with no baseline, no method and no name. We will not. Every number on this page is labelled as a target until a design partner consents to publishing a measured result.
- Baseline stated or the number is not published
- Method described so it can be challenged
- Escape rate reported alongside any suppression claim
- Consent required before any attribution
# closed loop, one panel
perceive substrate → tft → emitter stack → sub-pixel
plan deposition · anneal · transfer · test/bin
act recipe write-back (SECS-GEM)
sense mura ΔL* · sub-pixel opens · particles
optimise yield · ramp · scrap
log immutable audit entry ✓ signed
retrain engineer correction → model
Voices from the line
What the fab floor tells us
“We do not lose panels because nobody is watching. We lose them because the signal that mattered was buried under a thousand nuisance calls.”
“Ramp is the whole game. If a new product takes two quarters of manual tuning, that is two quarters of margin we never get back.”
“Transfer yield is our ceiling on microLED. Every dead emitter is a repair cycle or a scrapped backplane.”
Quotes are illustrative composites drawn from discovery interviews with process-integration, yield and quality engineers. Named references are [PLACEHOLDER] pending design-partner consent.
The economics
What the loop is measured on
Emiteon is priced and evaluated on the numbers a fab already reports. These are design-partner targets for the first twelve months of deployment.
$26BDisplay-fab automation, process control, inspection, test and fab software market
~14%Annual growth in the segments Emiteon plays in
135%Net revenue retention target from land-and-expand
99.9%Uptime target for the fab edge runtime
Figures marked as targets are design-partner objectives, not audited results. Company operating status, customers and outcomes are [ASPIRATIONAL] until independently verified.
FAQ
Questions fabs ask first
Through three gates. Shadow mode observes and predicts with no write-back; advisory mode recommends recipe, transfer and binning moves that a process-integration or yield engineer approves; graduated autonomy releases low-risk deposition, anneal and transfer control once measured accuracy and twin validation clear the bar. High-impact decisions stay human-in-the-loop.
Reducing false calls is the wedge, not a side effect. The mura-and-defect agent is trained on true-versus-false-call labels from your own inspection history, so it separates genuine large-area non-uniformity, sub-pixel opens and shorts, particles and stains from nuisance signals, and every call is traceable to the evidence behind it.
Per-tenant isolation with recipes, panel designs and defect images scoped to your tenant, encryption in transit and at rest, SSO/RBAC, an immutable yield/quality-grade audit log and an on-prem or air-gapped option. Fleet learning shares model improvements, never your recipes.
Deposition, photolithography, encapsulation and anneal tools, Mura/AOI inspection and metrology, array and cell test, mass-transfer and repair stations, robotic handling and MES — vendor-neutral via SECS-GEM/HSMS where the tool supports it, with REST and OPC-UA bridges elsewhere.
Every pixel, perfectly uniform.
Write the fourth shape with us
Bring the workflow. We will bring the method and the honesty about what it shows.