SEMICONDUCTOR
Every step a deviation
goes unexplained
adds to its cost.
Defects and drifts are often understood after more wafers and tool time have been spent. Xpdeep localizes the variables and temporal segments that drove the model output. Engineers can then evaluate whether that evidence supports an earlier process decision.
ILLUSTRATIVE PROCESS · THE FAB'S CONTROL SYSTEM REMAINS IN CONTROL
01 PROCESS STEP
The defect may be visible later.
The relevant process evidence may belong to an earlier step.
Semiconductor manufacturing chains combine process history, equipment state, recipes, temporal signals and inspection evidence.
Xpdeep can expose which variables and temporal windows carry the learned output.
ILLUSTRATIVE VARIABLES · NOT ALL PROCESSES
- PROCESS STEP N-2
- PROCESS STEP N-1
- PROCESS STEP N
- INSPECTION / OUTCOME
Xpdeep localizes the variables and temporal segments that drove the model output. Engineers can then evaluate whether that evidence supports an earlier process decision. Predictive evidence does not by itself establish the physical origin of a defect.
02 LOCALIZATION
The model should show
where in the signal
the decision was made.
- RAW TEMPORAL SIGNAL
- LOCALIZED WINDOW
- STRUCTURAL DEPENDENCY
- MODEL OUTPUT
On temporal data, Xpdeep can localize not only which variable mattered, but the segment and morphology that carried the model output.
The model localizes the learned driver.
The engineer names the physical cause.
POTENTIAL USES
- Artefact localization
- Process-deviation localization
- Measurement correction
- Fault attribution support
INSPECTION
Post-hoc maps estimate spatial input influence.
Xpdeep reads it from the model’s own structure.
KNOWN CONDITION
STRUCTURAL EXPLANATION · DOMAIN STATE: KNOWN
CHANGED CONDITION
ERROR BREAKDOWN · DOMAIN STATE: OUTSIDE
Xpdeep can expose which learned structures drove the output and whether the image remains inside the model’s learned domain.
Structural explanation · attributed domain state · error breakdown where relevant.
Post-hoc saliency methods approximate how input regions influenced an output from outside the model, and remain useful for exploration. Xpdeep’s architectural difference is that spatial input influence is returned by the model itself, alongside its domain state. With authorized access to the PyTorch architecture and weights, Xpdeep can structurally analyse the customer’s or supplier’s learned inspection model.
Structural integration requires read access to the PyTorch architecture and weights. Supplier-owned models require the supplier’s cooperation.
TECHNICAL BOUNDARY: Image-level prescription: not available; under study.
03 DOMAIN
A process or inspection model
should know when the current state
has moved beyond what it learned.
TEMPORAL DOMAIN
Graded distance by variable and time window
INSPECTION DOMAIN
Attributed domain departure / novel-class detection where appropriate
The objective is not a generic confidence score.
It is an attributed domain state that identifies what drove the departure.
EXAMPLES
- Unseen process condition
- Changed equipment behavior
- New defect morphology
- New visual class
- Process drift outside the trained domain
04 INLINE DECISION
Move the decision closer to the step
associated with the learned evidence.
TEMPORAL / TABULAR STAGE
Full decision chain including prescription where applicable.
- PROCESS STATE
- PREDICT
- EXPLAIN
- CHECK DOMAIN
- PRESCRIBE WHERE APPLICABLE
- JUSTIFY
- ACT / HOLD / ESCALATE
VISION / INSPECTION STAGE
Structural explanation + domain state + downstream decision.
- INSPECTION MODEL
- STRUCTURAL EXPLANATION
- DOMAIN STATE
- DOWNSTREAM DECISION
Prediction and structural explanation share one forward pass. Prescription is a separate inference, invoked when useful on temporal and tabular stages.
The surrounding manufacturing and control system remains in control.
Xpdeep changes what the learned model returns. It does not replace the fab’s process-control or inspection stack.
QUALITY
Not every deviation
should trigger the same response.
ROUTINE STATES
→ remain inside the system
TRUE EXCEPTIONS
→ HOLD / ESCALATE → EXPERT
When the model can state both its decision context and its domain, human review can focus on the cases that genuinely require intervention.
POTENTIAL OPERATIONAL VALUE
- Fewer unnecessary reviews
- Faster exception handling
- More equipment / process steps per expert
- Earlier intervention
- Less decision latency
DESIGN
Catch model and data problems
before they reach the line.
- INITIAL MODEL
- READ LEARNED STRUCTURE
- DIAGNOSE PROBLEM REGION
- TARGETED LOCAL EDIT (NATIVE)
- VERIFY EFFECT
REST OF MODEL FROZEN
Xpdeep can expose variables, temporal structures or learned regions that are not carrying the intended decision, allowing targeted engineering intervention before deployment.
POTENTIAL VALUE
- Cleaner model structure
- Fewer unnecessary inputs
- Earlier detection of model / data design issues
- Better KPI alignment
05 YIELD ECONOMICS
The value is not another defect score.
It is an earlier, better-attributed decision.
ECONOMIC SURFACES TO MEASURE
YIELD
Less value lost before the issue is understood
QUALITY
Predictive evidence localized in process history
SCRAP / REWORK
Potentially fewer downstream losses
THROUGHPUT
Less delay from unnecessary review or stoppage
ENGINEERING TIME
Faster diagnosis of model / process issues
Establish the application’s performance and economics against agreed KPIs.
The relevant model capabilities are already live.
TEMPORAL MODELS
Signal-level localization, structural explanation, domain awareness and prescription are live.
VISION MODELS
Structural explanation, attributed domain state and design-loop diagnosis are established capabilities.
PLATFORM
Core engines, XpViz, XpAct and the platform layer are live.
On time-series data, Xpdeep models achieve accuracy at least equivalent to standard deep learning and frequently superior.
Establish investigation time, detection performance and the effect of process decisions on yield and material loss against agreed KPIs.
INTEGRATION
Xpdeep changes the model.
Not your fab stack.
- EQUIPMENT / SENSORS / INSPECTION
- DIGITIZED PROCESS DATA
- XPDEEP MODEL
- FDC / MES / APC / CONTROLLER / QUALITY SYSTEM
Fault-detection and classification, inspection and process-control systems remain in place. Xpdeep provides structural model evidence and attributed domain state; temporal and tabular stages can also return explained constrained interventions.
No proprietary transport or logging framework imposed.
Semiconductor engineering boundaries.
EXPAND
- APC, FDC, MES, inspection systems and metrology remain in place.
- Structural integration requires read access to the PyTorch architecture and weights, or a Native model.
- Prescription applies to temporal and tabular stages; image-level prescription for inspection models is not available; under study.
- Predictive evidence does not by itself establish the physical origin of a defect.
- The customer’s surrounding system defines and authorizes actions.
- Attributed domain state covers the retained model inputs; alone, it does not authorize action or establish system safety.
- Structural justification does not automatically establish physical causality.
- Minimal means minimal feasible under the stated constraints and optimization procedure.
- Targeted editing and controlled correction require Native mode.
- System validation and certification remain with the customer and relevant authority.
- Reduced requalification scope requires application-specific evidence and acceptance.
START WITH ONE SEMICONDUCTOR DECISION
Start where a deviation
is expensive to understand too late.
PROCESS STATE
A temporal model tied to equipment or recipe behavior
DEFECT LOCALIZATION
A signal or model decision where the relevant region is not understood
INSPECTION DOMAIN
A vision model that needs explicit domain awareness
YIELD DECISION
A learned process / quality decision still reviewed manually
PROCESS ADJUSTMENT
A temporal / tabular stage where a bounded feasible change may matter
Run an Xpdeep model alongside your existing model for an agreed number of weeks. Compare performance on the same data against agreed KPIs, using your own validation chain.
An Xpdeep model is a PyTorch model with a different architecture. Customers can validate it using their own tools and test processes.
01 →
One funded decision
A model or decision loop the business already owns.
02 →
Agreed comparison
Data, duration and KPIs defined at scoping.
03 →
Evidence on your application
Side-by-side results using the customer’s validation process.
04
Define the extension
Identify the possible Phase 2 scope at the start; use Phase 1 evidence to determine its scope.