RF Module Test Data Analysis for Calibration Error Detection
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Solution Overview
Problem
Existing RF module manufacturing and testing processes face challenges in identifying and addressing various sources of errors, including calibration, maintenance, and technician errors, which are time-consuming and provide limited insight into error identification.
Innovation Solution
A system and method for analyzing RF module test data to determine error sources, including generating visualizations and transmitting alerts or recalibration settings to user devices, and optimizing RF module combinations based on test result data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple test stations are used to test RF modules, then testing capacity and productivity are improved, but identifying the source of errors becomes more difficult due to multiple potential error sources
Solution Approach 1:
The system implements feedback by collecting test result data from multiple test stations and analyzing it to generate determinations about error sources. The processor receives test data objects containing test result data, analyzes trends across multiple stations, and provides feedback information identifying whether errors are due to calibration, maintenance, or technician issues at specific stations.
Solution Approach 2:
The system segments the error analysis by test station, creating separate test data objects for each station. This segmentation allows the system to compare performance across individual stations and identify which specific station is experiencing calibration drift, maintenance issues, or technician errors, rather than treating all test stations as a single unit.
2Device complexity
If traditional error identification methods are used, then the process is simpler, but it is time-consuming and provides limited insight into error sources
Solution Approach 1:
The system performs self-service by automatically analyzing test result data from multiple test stations and generating determinations about error sources without requiring manual intervention. The processor automatically compares test data, identifies trends, and determines whether calibration, maintenance, or technician errors are present, eliminating the need for manual error investigation.
Solution Approach 2:
The system introduces an intermediary processing layer between test data collection and error identification. The processor acts as an intermediary that receives raw test result data, performs automated analysis, and generates structured determinations about error sources, providing a bridge between simple data collection and complex error analysis.
3Loss of time
If automated error analysis is implemented, then error identification speed and insight are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential information needed for error identification from the test result data. Rather than processing all raw data, the processor extracts key metrics and trends that indicate calibration drift, maintenance issues, or technician errors, simplifying the data processing requirements while maintaining effective error identification.
Data Source
AI summary
Systems, apparatuses, and associated methods are provided for testing, analyzing, and assembling radiofrequency (RF) modules. An example system includes a test station for testing an RF module, including testing mechanical, chemical, and/or electrical parameters of the RF module. The testing may involve various sources of error, and the system determines one or more types of error from the test results as well as how to address the error. From completed testing, test results may be utilized in view of expected test results to improve simulations and to determine how two or more RF module may be combined in an RF assembly.