Robot Cell Geometry Calibration for Compact SLT Handling
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Solution Overview
Problem
System-level testing (SLT) systems require large footprints to achieve sufficient testing speed and throughput, making them inefficient in terms of space and potentially damaging devices due to the need for precise robotics and complex setups.
Innovation Solution
A modular test system with a calibrated robot that determines the geometry of cell arrangements, calculates offsets for precise movement, and operates in multiple stages with varying precision levels to accommodate different testing requirements, allowing for compact design without sacrificing speed or throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SLT systems use large footprints with complex setups, then testing speed and throughput are sufficient, but space efficiency deteriorates and device damage risk increases
Solution Approach 1:
The system is divided into multiple stages (first stage, second stage, third stage) with different precision levels. The first stage performs coarse positioning with lower precision requirements, the second stage performs intermediate positioning, and the third stage performs fine positioning with high precision. This segmentation allows the system to achieve high throughput without requiring all components to occupy high-precision space simultaneously, thereby reducing the overall footprint while maintaining testing speed.
Solution Approach 2:
Different stages of the system are assigned different precision characteristics. The first stage uses lower precision positioning suitable for high-speed operations, while the third stage uses high precision positioning for final device placement. This local differentiation of quality allows the system to maintain high productivity in the early stages without requiring high precision throughout the entire system, thus reducing space requirements while maintaining throughput.
2Productivity
If traditional SLT systems use large footprints with complex setups, then testing speed and throughput are sufficient, but device damage risk increases
Solution Approach 1:
The system dynamically adjusts precision requirements across different stages. The first stage operates with lower precision and higher speed, reducing the time devices are exposed to potential damage risks. The third stage operates with high precision only when needed for final placement. This dynamic allocation of precision reduces overall device exposure to harmful factors while maintaining high throughput.
Solution Approach 2:
The first stage performs preliminary positioning and sorting of devices before they reach the high-precision third stage. By pre-positioning devices in the lower-precision first stage, the system reduces the time devices spend in the high-precision, potentially more vulnerable third stage, thereby reducing device damage risk while maintaining productivity.
3Manufacturing precision
If calibrated robotics are used to achieve precise movement, then positioning accuracy improves, but system complexity increases
Solution Approach 1:
The positioning system is segmented into multiple stages with different complexity levels. The first stage uses simpler, lower-precision positioning mechanisms, while the third stage uses more complex, high-precision calibrated robotics. This segmentation allows the system to achieve high positioning accuracy only where necessary, reducing overall system complexity while maintaining manufacturing precision for critical operations.
Solution Approach 2:
High precision calibrated robotics are applied locally only in the third stage where final device placement requires high accuracy. The first and second stages use simpler positioning mechanisms. This local application of high precision reduces the overall complexity of the system while maintaining the necessary manufacturing precision for critical positioning operations.
Data Source
AI summary
An example method, such as a calibration method, includes: determining a geometry of an arrangement of cells that is perceived by a robot configured to move devices into, and out of, the cells; determining an expected location of a target cell among the cells; determining an offset from the expected location that is based on the geometry that is perceived by the robot; and calibrating the robot based on the offset.