Multiple Input Transition Cell Fault Model for Semiconductor Defect Detection
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Solution Overview
Problem
Conventional fault diagnosis models in semiconductor manufacturing are limited to modeling faults with a single signal transition input, making it challenging to detect and diagnose defects in complex transistor designs with subtle marginal behaviors, especially as circuit designs become increasingly complex and defects involve multiple input transitions.
Innovation Solution
The implementation of a multiple input transition cell-aware test model (MTCFM) that models defect modes with transition signals on multiple input pins, enabling the detection of different and potentially more defect modes, thereby increasing the probability of accurately identifying defect modes and determining their location within the circuit design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fault diagnosis models with single signal transition input are used, then the model complexity is low, but the defect detection capability is insufficient for complex transistor designs
Solution Approach 1:
The patent segments the fault diagnosis process into multiple distinct defect modes (e.g., stuck-at-0, stuck-at-1, transition faults) and models each separately using different test vectors. This allows comprehensive defect detection without creating a single overly complex model, as each segmented defect mode can be analyzed independently with appropriate test patterns.
Solution Approach 2:
The patent extends the conventional single-input transition model by introducing multiple input transitions simultaneously. Instead of analyzing one input transition at a time, the model now considers combinations of multiple input transitions, adding a dimensional aspect to the fault analysis that enables detection of complex defects involving multiple inputs.
2Measurement precision
If conventional single transition fault models are used, then the testing process is simple, but subtle marginal defects in complex transistors cannot be detected
Solution Approach 1:
The patent performs preliminary classification of defects into specific defect modes before detailed analysis. By pre-categorizing potential defects (stuck-at faults, transition faults, multiple input faults) and preparing corresponding test vectors in advance, the system can precisely target subtle marginal defects without requiring complex real-time analysis during testing.
Solution Approach 2:
The patent introduces an intermediate layer of defect mode classification between the physical defect and the test response. This intermediary classification system maps complex transistor defects to standardized defect modes, enabling precise detection of subtle marginal behaviors through a structured intermediate representation that bridges the gap between physical defects and test outcomes.
3Reliability
If multiple input transition defect modes are modeled, then more defect modes can be detected, but the correlation and analysis process becomes more complex
Solution Approach 1:
The patent segments the correlation process by matching observed test failures to specific predefined defect modes. Instead of attempting to correlate all possible multi-input transitions simultaneously, the system divides the analysis into discrete defect mode categories, each with its own correlation criteria, making the overall complex process manageable through segmentation.
Solution Approach 2:
The patent changes the parameters of the test model to accommodate multiple input transitions by introducing additional variables representing different input combinations. This parameter expansion allows the model to detect more defect modes while maintaining a systematic approach to correlation, where each parameter change corresponds to a specific defect mode hypothesis.
Data Source
AI summary
Systems and methods disclosed include receiving defect data from a test of a semiconductor device comprising a circuit, the circuit comprising a cell, the cell comprising a first input, a second input and an output, and modeling a first plurality of cell defect modes of the cell with a first multiple input transition cell fault model (MTCFM), the cell defect modes associated with a first signal transition on the first input, and a second signal transition on the second input or the output. Systems and method further include correlating the first plurality of cell defect modes to the defect data to produce a probability of each of the first plurality of cell defect modes matching the defect data, and providing, to a user, an indication of each of at least one of the first plurality of cell defect modes having the probability exceeding a defect probability threshold.