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ViewTool(KAI Analysis Tool) for Semiconductor Analysis Now Available

Semiconductor Test Data Analysis System "KAI Analysis System"

High-speed analysis of large-scale semiconductor device test data to support quality improvement, anomaly detection, and yield improvement.

Key Features

High Speed

Processes large volumes of data with practical performance.

Scalability

Modules and functions can be added easily.

Security

Supports license management, authentication, and access control.

KAI Analysis System Configuration

The KAI Analysis System consists of KAI Server and KAI Analysis Tool.

KAI Server mainly receives test data sequentially from testers and registers it in an internal database.
KAI Server analyzes data to detect tester measurement anomalies and failures, product characteristic abnormalities, and equipment abnormalities at an early stage.

KAI Analysis Tool connects to the KAI Server database and performs various test data analyses.

KAI Analysis System configuration for collecting tester data in KAI Analysis Server and analyzing it with KAI Analysis Tool

Use Cases

Example of analyzing semiconductor test data with KAI Analysis Tool

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Detailed Description of the KAI Analysis System

KAI_Analysis_Tool_Client is a PC-based tool for analyzing test data. The purpose of this tool is to analyze test data to achieve the following.

  1. Improvement of product quality
  2. Yield improvement
  3. Early detection of manufacturing anomalies
  4. Reduction of manufacturing and test costs
System overview showing semiconductor tester data collected by KAI Analysis Server and analyzed through KAI Analysis Tool
Overview of the KAI Analysis System

Deployment Model

KAI_Analysis_Tool_Client is operated together with KAI_Analysis_Server_System. KAI Analysis Server connects to testers and upstream systems, receives test data and metadata, and writes them to an internal database. KAI_Analysis_Tool_Client reads data from the KAI_Analysis_Server database and performs various types of analysis.

By connecting to the KAI_Analysis_Server database, KAI_Analysis_Tool_Client can handle large volumes of data. It can also read and analyze test data files such as STDF independently without accessing the database. However, in that case, analyses that require time-series data, such as state transition analysis, or analyses that identify patterns from large volumes of data become difficult.

1Basic Statistical Analysis

Analyzes the distribution of parametric data by lot and wafer, wafer surface distribution (heat map), calculation of statistics, and data processing.

It also supports analyses such as outlier detection and defect classification based on test data distributions.

Basic statistical analysis screen showing Wafer site data, distributions, and histograms
Basic Screen / Site-by-Site Display

2Correlation Analysis

Analyzes correlations among parametric test data. Data points (devices/chips) that deviate from correlations can also be classified as outliers or defects. Correlations can be analyzed not only within the same wafer, even across processes, but also between wafers and between lots for various analysis purposes.

2D analysis screen for checking correlations and outliers between parametric test data
2D Analysis

3Various Views

Wafer maps (heat maps), pass/fail maps, histograms, QQ plots, category maps, and list views can be displayed, making it easy to notice anomalies immediately when they occur.

Analysis gallery displaying Wafer Maps, histograms, QQ plots, and PF Maps
Gallery Display of Various Views

4Control Charts

Anomalies can be detected using Xbar-R/S control charts for statistics and parametric data. EWMA and CUSUM analyses are also supported.

Xbar-R/S control chart screen for detecting variation and anomalies in semiconductor test data
Xbar_R_S Control Chart

5Region-Segmented Control Charts

The wafer area can be divided into regions (10 or 16 segments), and statistics are calculated for each region. By analyzing these values with control charts, anomalies can be detected early.

There are four region-division methods. Target data include yield rate, defect category rate, and parametric statistics such as mean, median, IQR, and SD.

Wafer control chart screen comparing test data trends by wafer region to detect anomalies
Wafer Control Chart
Diagram explaining wafer region segmentation for analyzing yield, failure category rates, and statistical values
Region Segmentation Methods and Target Data
When an anomaly is suspected, detailed analysis can be performed.

Display Examples

Examples of region-segmented maps, PF maps, category maps, and numerical maps.

Examples of segmented-region maps, PF maps, category maps, and numerical maps
Examples of Various Map Displays

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