neighborhood correlation
**Neighborhood correlation in testing** is the **analysis of spatially adjacent die behavior on wafer maps to detect statistical outliers and latent defect risk even when individual dies pass nominal limits** - it leverages local context to improve screening decisions.
**What Is Neighborhood Correlation?**
- **Definition**: Compare a die's electrical metrics against nearby dies to identify anomalous deviation patterns.
- **Context Principle**: Adjacent dies often share similar process conditions; strong deviation can signal hidden issues.
- **Typical Use**: Part Average Testing and maverick detection workflows.
- **Decision Output**: Additional screening, re-bin, or reject candidate outlier dies.
**Why Neighborhood Correlation Matters**
- **Latent Defect Detection**: Finds risky dies that pass absolute specs but are statistically abnormal.
- **Escape Reduction**: Prevents weak units from reaching field operation.
- **Process Insight**: Reveals localized wafer excursions and systematic anomalies.
- **Quality Improvement**: Strengthens outgoing reliability beyond simple threshold checks.
- **Data Utilization**: Converts wafer-map spatial structure into actionable quality signals.
**Analysis Methods**
**Local Sigma Rules**:
- Flag die values deviating from neighborhood mean by configurable sigma limits.
- Simple and effective for maverick screening.
**Spatial Clustering**:
- Detect contiguous abnormal regions indicating process defects.
- Supports root-cause investigations.
**Hybrid Risk Scoring**:
- Combine absolute limits, neighborhood statistics, and historical failure propensity.
- Improve precision of reject decisions.
**How It Works**
**Step 1**:
- Build neighborhood statistics for each die from wafer test measurement maps.
**Step 2**:
- Score outlier risk and apply additional quality rules for suspect dies before final bin release.
Neighborhood correlation in testing is **a context-aware quality safeguard that catches statistically suspicious dies before they become field failures** - combining local spatial analytics with standard limits significantly improves screening effectiveness.