OUR MISSIONExploring AI-era semiconductor engineering analysis and verification.
How does it advance investigations?
The Agent inspects imagery and prior runs, executes standard checks first, and runs targeted analyses on demand.Grounding decisions in accumulated evidence and verified engineering method specs.
Regions of interest, tool calls, and computed metrics displayed side-by-side on one screen.
Every hypothesis update and next step is justified by accumulated observations and specs.
Review, adjust parameters, or take over control at any point; open questions remain highlighted.
How does it integrate into my existing environment?
Deployment Options: Fab Private Cloud / On-Premises · Equipment Edge Computing.
Data and computation stay entirely within customer perimeter.
Equipment, data, and engineers collaborate continuously inside the same Fab.
Aligning lot, time, coordinate, and recipe versions first, then collaborating with existing tools to deepen PE and PIE investigations.
Align · Compare · Correlate · Model on Demand
Handing candidate correlations, raw evidence, and recommended validation steps directly to PE and PIE.
How does it help engineers resolve issues?
Automates routine analysis across more wafers, time windows, and operating conditions, surfacing valuable clues and candidate causes. Engineers inspect evidence layer-by-layer, steer investigation directions, and advance findings into tuning and validation plans.
Across Wafers · Time Windows · Process Conditions
Supporting & Opposing Evidence · Pending Checks
Priority Checks · Parameter Directions · Validation Steps
Observations → Checks → Computations → Findings → Recommendations
Executes defined inspection routines, eliminating repetitive data extraction, coordinate alignment, and manual formatting.
Structures comparisons across wafers, historical time windows, and operating splits to expand investigative coverage.
Aggregates spatial patterns, parameter deltas, and candidate causes to support the next check and engineering proposal.
Inputs, method specs, quantitative metrics, and decision rationale remain fully auditable, adjustable, and steerable.
Executes defined checks and computations automatically to reduce repetitive manual overhead.
What do I receive at the end?
A comprehensive report outlining current diagnosis, recommended actions, and validation procedures, with every suggestion tied to analytical traces, evidence, and open questions.
Where do its engineering judgments originate?
Derived from process specs, equipment manuals, standard analytical practices, and cleanroom field experience,
forming reviewable, version-controlled method specs validated against precise applicability boundaries.
Team Knowledge AssetSpec Files · Protocols & Boundaries
Agent ExecutionSpecs Guide Action · Traces Stored with Output
Retains current investigation evidence, audits novel failure modes and methods, and publishes updated spec packages.
Ingests process specifications, equipment manuals, and standard analytical SOPs; historical lots refine and harden rules.
Method specs govern how judgments are formed; tool contracts define what actions can be executed. New engineering tasks simply plug in validated tools.
Wafer Analysis: Comparability specs, scientific computing tools, and evidence standards together power continuous investigation.
Codifies senior engineers' intuition and historical lot post-mortems into transparent, reviewable, and reusable team assets.
The Harness bridges specifications with ingested lot data, compute tools, and runtime verification; the Agent operates strictly within valid bounds.
Expand to new engineering tasks with modular spec packages, tool contracts, and benchmark lots while sharing the proven runtime harness.
Engineering expertise is managed as modular specs, published after expert review, and reused across future investigations.
TranslatingFab engineering expertise and senior analytical methodsinto applicability prerequisites, scientific computing routines, and rigorous evidence standards. Our Agent architecture advances along these engineering constraints to form a closed-loop investigation.
Incorporates tool state, recipe parameters, review optics, and comparison goals into the run, pinpointing which clues warrant DOE and what data must be fetched.
Verifies population sample sizes, active inspected areas, analysis metrics, and baseline definitions to clearly distinguish observations from engineering explanations.
How do I audit its conclusions?
From full-wafer imagery and recipe parameters to actionable engineering recommendations.
Inputs, method specs, computations, and execution traces are auditable layer by layer.
Every investigative branch leads directly to its underlying evidence.
Audit inspection conditions and intra-wafer progression first, then leverage recipe and tool logs to isolate candidate hypotheses. Recommended parameter adjustments require direct empirical evidence and validation DOEs.
Retains supporting evidence, gaps, and non-excluded candidate hypotheses. Recommendations provided for engineer review; correlated leads still require validation.
What engineering experience do we want to deliver?
Starting from Fab image analysis and yield learning, we bring engineers and specialized AI agents onto a unified workbench to investigate continuously, compute rigorously, and verify every finding together.
Transforming one-off reports into an ongoing engineering journey. New problems, fresh lot data, and emerging discoveries connect seamlessly inside the same shared context.
RESOURCES & DOCUMENTATION
Bridging wafer imagery, unit process steps, and tool recipes to empower PE and PIE investigations.
Organizing SOPs, equipment manuals, and cleanroom field heuristics into reviewable, version-controlled method specs.
Understand current interactive demo capabilities, our roadmap for continuous investigation, and scope for initial fab pilots.
Define the engineering problem, inputs, and target deliverables for your team to assess incremental ROI.