Computational Memory Test Flow Optimization for Lower Yield Loss
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Solution Overview
Problem
Identifying the best combination of test item conditions for memory testing during the production phase to meet defective part count goals, minimize yield loss, and reduce time and cost is technically challenging due to the large number of potential test items and conditions, leading to issues like overkill and missed defects.
Innovation Solution
A computer-implemented method determines a test flow by receiving input on test items, candidate conditions, and benchmark defects, eliminating ineffective items to reduce yield loss and DPPM, while ensuring effective detection of defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple test items with many candidate conditions are used to screen defects, then defective part detection capability is improved, but yield loss increases due to overkill
Solution Approach 1:
The patent applies parameter changes by systematically varying test item conditions (such as voltage levels, current thresholds, temperature ranges) to identify the optimal combination that detects defects while minimizing false positives. The computer-implemented method evaluates multiple parameter sets across 500+ test items with 20+ candidate conditions each, selecting parameters that achieve DPPM targets without excessive yield loss.
Solution Approach 2:
The patent implements feedback mechanisms by using development and qualification phase test results to inform production phase test flow configuration. The system continuously monitors defect detection performance and yield loss metrics, adjusting test item conditions based on feedback from actual manufacturing data to optimize the balance between defect detection and yield preservation.
2Reliability
If comprehensive test item conditions are applied to meet DPPM targets, then defective part screening is improved, but test time and cost increase
Solution Approach 1:
The patent extracts only the most critical and effective test items from the comprehensive set of 500+ candidate test items. The computer-implemented method identifies and eliminates redundant or less effective test conditions, retaining only those that provide the highest defect detection value. This extraction process reduces the test flow from potentially 20,000+ test combinations to a optimized subset that meets DPPM targets with reduced test time.
Solution Approach 2:
The patent applies partial action by implementing a streamlined test flow that uses only the necessary portion of available test items and conditions. Rather than executing all possible test combinations, the system selectively applies a subset of test items with the most relevant conditions for the specific memory device being tested, achieving adequate defect screening without the excessive time cost of comprehensive testing.
3Measurement precision
If numerous test items are used to detect defects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the comprehensive test flow into distinct categories and groups of test items based on their functional purposes and target defects. The computer-implemented method organizes 500+ test items into logical segments (such as electrical characteristics, physical properties, performance metrics) and selectively applies segments based on the specific device being tested. This segmentation maintains high measurement precision while reducing overall system complexity by avoiding the need to configure and manage all possible test items simultaneously.
Data Source
AI summary
Technology for determining a test flow for testing memory during a production phase. The following are accessed: a list of test items with a number of candidate test item conditions for each test item, defects (e.g., bad blocks) detected by each test item condition, a list of benchmark defects (e.g., bad blocks), and judgement criteria. The defects (e.g., bad blocks) for the candidate test item conditions may be compared with the benchmark defects (e.g., bad blocks) in view of judgement criteria. Based on the comparison, at least one test item may be eliminated resulting in a set of remaining test items. A production phase test flow having a test item condition for each remaining test item may be generated.