Silicon operations co-pilot

Turn wafer test data into a live operations co-pilot.

WaferWise.AI unifies STDF, IG-XL, SmarTest, and PXI data into an ML-powered workbench—profiling every device, reducing unnecessary tests, and prescribing next-best actions for yield and reliability.

Built for data + test engineers Device DNA across the lifecycle Static delivery for SEO + speed

Talk to us See how it works

01 · Data ingestion

Ingests STDF, CSV/JSON, and handler logs into a governed lake with schema checks, range guards, and unit validation.

02 · ML engine

Classification, clustering, anomaly detection, and forecasting with built-in feature selection, AutoML, and model lineage.

03 · Prescriptive output

Contextual recommendations to trim test volume, route units by sigma profile, and surface binning actions with ROI.

Live

Device DNA at a glance

Each lot and wafer builds an analog DNA profile with parameter trends, outlier flags, and downstream fit suggestions.

Profiling: Min/Max, σ, skew/kurtosis, correlation, temporal drift.
Controls: Schema + unit validation, checksum checks, metadata tagging.
ML options: Expert-picked or model-ranked features, transfer learning for test versioning.
Decisions: Bin routing, test skip candidates, qualification guidance.

Supported sources Advantest · Teradyne · Cohu · NI/Emerson · Chroma

Platform architecture

Three engines aligned to how semiconductor teams work.

Collect, learn, and prescribe—all with human-in-the-loop control for test, data, and ML teams.

Engine 1 · Data workbench

Unifies timestamped device, lot, and site data with parameter readings from every tester stream.

Streaming + batch ingest (STDF, logs, CSV/JSON).
Schema, unit, range, and checksum validation.
Profiling: percentiles, σ, outliers, correlations.
Logical source layer for immutable raw data.

Engine 2 · ML engine

Predictive and descriptive analytics with transparent lineage and guardrails.

Classification, regression, clustering, anomaly detection, forecasting.
AutoML baselines plus custom models; GPU-ready.
Feature selection (model-ranked or engineer-picked).
Transfer + federated learning across test versions.

Engine 3 · Recommendation

Prescriptive guidance to cut test volume, improve outgoing quality, and route devices.

Rules + thresholds with auditability and role-based access.
Interactive dashboards and exportable reports.
Anomaly summaries with ROI and time saved.
MES/QA integration ready for automated flows.

What you unlock

From data hygiene to prescriptive actions.

Built to shrink manual workflows and deliver decisions engineers can trust.

Data integrity first

Range guards, null checks, time-series continuity, and cross-field consistency checks tuned to wafer, lot, and unit IDs.

Analog device DNA

Continuous quality scoring beyond pass/fail to capture the nuance between edge-pass and hero devices.

Adaptive test strategy

Test skip recommendations and selective re-runs guided by model confidence and human overrides.

Drift + degradation alerts

Temporal monitoring on key parameters (Iddq, Vout, leakage, THD) with alerting hooks.

Qualification foresight

Use parametric signals to forecast qualification risk and bin routing by sigma profile.

Transparent ML

Versioned models with confusion matrices, precision/recall, and feature importance surfaced to engineers.

MVP path

Phase 1–3 ready

Data ingest + profiling · ML baselines · Dashboard + exportable reports.

Reliability

Governed pipeline

Immutable raw layer, RBAC, and audit trails for every recommendation.

Collaboration

Model Repository

Share custom and purpose-built models with your co-workers.

Talk with us

Bring intelligence to your test floor.

Tell us about your testers, data formats, and current pain points. We’ll map a rollout plan aligned to your production flows.

What we’ll cover: Ingestion targets, ML priorities (classification vs. anomaly), and recommendation workflows.
Who should join: Test engineering, data engineering, and QA/ops stakeholders.