Semiconductor · ChipSense

Find the defect and its root cause before the lot ships

ChipSense is a multi-agent system for inline defect classification, process-drift detection, and yield root-cause analysis. It reads your inspection, metrology, and MES data, ranks likely causes, and puts every proposed process change in front of an engineer for sign-off.

Request a strategy call See the ChipSense architecture

Engagement snapshot

Typical engagement: 2-4 week assessment, then 12-20 weeks to a first production workflow
First deliverable: Scoped agent architecture and a written go/no-go recommendation
Integrates with: MES, FDC, metrology and inspection tools
Human in the loop: Process engineers approve every recipe or parameter change; agents rank and explain

Full engagement tiers

What ChipSense is

ChipSense is our semiconductor agent system — a reference architecture we customize, build, and deploy inside your existing inspection, metrology, and MES stack. It is not an off-the-shelf product you buy: you own the deployment, and we run design, build, evaluation, and handover.

See how we deliver

Signals it ingests

WAFER IMAGES
TOOL SENSORS
MES EVENTS
PROCESS PARAMS

01Capabilities

What ChipSense Can Do

Purpose-built AI agent capabilities for semiconductor.

01 / 06

Defect Detection

Sub-micron defect classification using deep-learning vision models tuned on your own inspection corpus. Published implementations reach 97-99% classification accuracy at production throughput.

ChipSense capability

02 / 06

Design Verification

Automated design rule checking and layout verification that validates complex chip designs against manufacturing constraints in a fraction of the time.

ChipSense capability

03 / 06

Yield Optimization

Continuous process analysis that identifies yield-limiting factors and recommends parameter adjustments to maximize production output.

ChipSense capability

04 / 06

Process Control

Real-time monitoring and adaptive control of fabrication parameters to maintain tight tolerances across hundreds of manufacturing steps.

ChipSense capability

05 / 06

Wafer Inspection

High-throughput automated visual inspection that scans entire wafers for surface defects, pattern anomalies, and contamination at production speed.

ChipSense capability

06 / 06

Supply Chain Intelligence

Predictive supply chain analytics that forecast material demand, identify potential disruptions, and optimize inventory levels across the fab.

ChipSense capability

02Process

How ChipSense Works

A structured path from signal ingestion to measurable production impact in semiconductor.

Data Acquisition

ChipSense integrates with inspection tools, metrology equipment, and process control systems to capture high-resolution manufacturing data at every stage of the fab process.

PHASE 01 / 03

Multi-Agent Analysis

Specialized agents simultaneously analyze defect patterns, process drift, yield trends, and design compliance — correlating data across the entire manufacturing chain.

PHASE 02 / 03

Optimization & Reporting

Actionable insights are delivered as process adjustments, defect classifications, and yield improvement recommendations directly to engineering teams and control systems.

PHASE 03 / 03

03Use Cases

Real-World Applications

See how ChipSense solves critical challenges in semiconductor.

ApplicationChallengeAgent solutionOutcome

Application

Automated Visual Inspection

Challenge

Manual wafer inspection is slow, inconsistent, and cannot keep pace with advanced node production volumes.

Agent solution

ChipSense deploys vision agents that classify defects in real time with sub-micron precision, operating continuously without fatigue.

Outcome

Deep-learning machine vision reaches ~97-99% defect-classification accuracy at production throughput, with consistent inspection that does not fatigue.

Application

Design Rule Checking

Challenge

Complex multi-patterning designs require exhaustive verification that takes days using traditional EDA tools.

Agent solution

AI agents accelerate DRC by learning common violation patterns and prioritizing checks based on historical failure data.

Outcome

Faster verification cycles and earlier surfacing of likely violations, with every prioritized check engineer-reviewed before sign-off.

Application

Predictive Yield Modeling

Challenge

Yield loss root causes are difficult to isolate in processes with hundreds of interacting variables.

Agent solution

ChipSense builds causal models that trace yield excursions back to specific process steps and equipment conditions.

Outcome

ML root-cause analysis can reduce yield detraction by up to ~30%, where yield loss and test account for 20-30% of production cost.

Application

Equipment Calibration

Challenge

Maintaining precise equipment calibration across a fab is labor-intensive and drift between calibrations causes quality issues.

Agent solution

AI agents continuously monitor equipment output signatures and trigger predictive recalibration before drift causes defects.

Outcome

Fewer drift-driven excursions and unplanned recalibrations, contributing to AI-at-scale manufacturing cost reductions of up to ~17%.

Recognize any of these in your operation? Send us the workflow, its monthly volume, and the systems it touches — we will tell you which parts an agent should own and which should stay human.

Request a strategy call

04Architecture

Multi-Agent Collaboration

How specialized agents coordinate inside ChipSense.

▸ AGENT TOPOLOGYChipSense

▸ Input signals04

WAFER IMAGESTOOL SENSORSMES EVENTSPROCESS PARAMS

▸ ChipSense core04

PlannerRouterMemoryRetrieval

▸ Specialist agents04

Inspection AgentAnalysis AgentOptimization AgentReporting Agent

Inputs

4 industry signals

Orchestration

ChipSense core

Agents

4 specialists

Inspection Agent

Captures and classifies wafer defects in real time

Analysis Agent

Correlates defect data with process parameters

Optimization Agent

Recommends process adjustments for yield improvement

Reporting Agent

Generates fab-wide quality and yield dashboards

05Impact

Operational outcomes we target

Target ranges and today's baseline, both drawn from published research. Actual results depend on your data, systems, and scope.

▸ TARGET

Less yield detraction with ML root-cause analysis (up to)

30%

Source · McKinsey

▸ TARGET

Defect-classification accuracy in published deep-learning vision studies (97-99%)

99%

Source · Peer-reviewed machine-vision literature

▸ TARGET

Lower semiconductor manufacturing cost with AI at scale (up to)

17%

Source · McKinsey, 2021

Where the baseline sits today

Published figures describing the current state of the industry — not results VelocityMind has delivered.

▸ BASELINE

Of total production cost is yield loss and test today (20-30%)

25%

Source · McKinsey

06Case Study

Representative results

A representative ChipSense scenario modeled on published industry benchmarks.

CASE FILE · Semiconductor

~30% less yield detraction

Representative outcome · benchmark-based

Lifting yield with inline defect analysis

A representative fab deploys ChipSense for inline machine-vision inspection and ML root-cause analysis. Grounded in McKinsey semiconductor research and peer-reviewed defect-classification studies (over 97-99% accuracy), the scenario targets up to ~30% less yield detraction, where yield loss and test account for 20-30% of production cost.

Read the ChipSense case file

07Get Started

Talk to us about ChipSense

Bring one workflow you want fixed. In 30 minutes we will tell you whether an agent system is the right answer for it — and roughly what it would take.

Request a strategy call See the ChipSense case file

No commitment · We reply within one business day

▸ WHAT HAPPENS NEXT

01Initial response: Within one business day
02Strategy call: 30-45 minutes, no commitment
03Roadmap draft: 2-5 business days after the call