cs.AIOct 26, 2026
SaveZero-Shot Multi-Class Wafer Map Classification with Rule-Ordered Prompts for Large Multimodal Models
Organizations: Fudan University Shanghai, China
Abstract
Wafer-map defect classification often requires labeled data and retraining when production conditions change. We present a training-free six-class framework that combines connected-component highlighting with morphology-, location-, and exclusion-aware prompts for a frozen large multimodal model. On Mixed-WM38 (6,866 maps), multi-region prompting achieves 92.9% macro and 93.8% overall accuracy. On WM811K (9,000 maps), it achieves 79.1% and 81.3%, respectively. Eight alternative rule orders achieve 90.1-91.9% overall accuracy, compared with 93.8% for the selected order.
Figures & tables
Figure 1. Representative raw wafer maps of the six target defect patterns from Mixed-WM38 ( 18 ) . White pixels denote failed dies, gray pixels denote non-defect wafer areas, and black regions denote the background. Representative raw wafer maps for Center, Donut, Edge, Local, Random, and Scratch defect patterns.
Figure 2. Overview of the training-free image-text classification framework. The visual input is paired with a textual prompt, and a frozen LMM processes the image-text pair once and returns exactly one label. A workflow combining a visually prompted wafer map and ordered textual class rules as input to a frozen LMM, which returns one of six defect labels.
| Class | Positive morphology | Spatial anchor | Exclusion condition |
|---|---|---|---|
| Center | Dense, compact, predominantly solid cluster | Centered or near wafer center | Exclude hollow, annular, rim-aligned, elongated, and wafer-wide patterns. |
| Donut | Ring, hollow center, arc, crescent, C-shape, spiral, or partial annulus | Around or partly surrounding wafer center | Exclude solid central clusters without a clear opening. |
| Edge | Continuous or fragmented arc, peripheral band, rim-following segment, or edge ring | Attached to and aligned with the circular boundary | Boundary contact alone is insufficient; Exclude inward compact clusters and non-circumferential elongated traces. |
| Local | Thick, compact, solid clump or polygonal aggregation | Non-central interior, or boundary-near but extending inward | Exclude rim-following arcs, wafer-wide patterns, central clusters, and elongated traces. |
| Random | Large block-like regions with broad interior coverage and little continuous gray area | Across much of the interior without rim confinement | Exclude local clusters, annular structures, elongated traces, and rim-aligned arcs or rings. |
| Scratch | Long, narrow, line-like, stripe-like, diagonal, or aligned broken chain | Any wafer location | Exclude compact blobs with short tails and arcs whose principal direction follows the rim. |
Table 1. Class-semantic rules used in the textual prompt.
Figure 3. Visual grounding of two pairwise exclusion boundaries in the class-semantic rules. Center and Donut differ in solid versus hollow morphology; Edge and Local differ in rim alignment versus inward compactness. Pairwise visual comparison of Center versus Donut and Edge versus Local, showing the shared location cues and the contrasting exclusion boundaries.
| Method | C | D | E | L | R | S | Macro | Overall |
|---|---|---|---|---|---|---|---|---|
| Raw | 9.5 | 97.1 | 2.8 | 11.7 | 97.9 | 46.8 | 44.6 | 37.5 |
| Single | 99.6 | 97.3 | 95.6 | 98.0 | 70.1 | 94.0 | 92.4 | 93.3 |
| Multi | 97.6 | 98.7 | 96.8 | 92.1 | 78.5 | 93.9 | 92.9 | 93.8 |
| Names-only | 69.7 | 39.4 | 90.9 | 64.2 | 24.7 | 60.5 | 58.2 | 63.6 |
Table 2. Accuracy (%) on Mixed-WM38.
| Variant | C | D | E | L | R | S | Macro | Overall |
|---|---|---|---|---|---|---|---|---|
| Default | 97.6 | 98.7 | 96.8 | 92.1 | 78.5 | 93.9 | 92.9 | 93.8 |
| Center-first | 92.1 | 96.3 | 94.0 | 93.0 | 76.5 | 91.1 | 90.5 | 91.3 |
| Edge-before-Scratch | 94.5 | 97.7 | 92.3 | 93.7 | 77.5 | 87.0 | 90.4 | 91.0 |
| Local-before-Edge | 96.1 | 97.5 | 89.0 | 94.0 | 71.7 | 91.0 | 89.9 | 90.1 |
| Local-before-Scratch | 95.5 | 98.1 | 92.0 | 96.0 | 80.1 | 80.5 | 90.4 | 90.8 |
| Random-before-Edge | 94.0 | 96.9 | 91.5 | 95.5 | 80.3 | 92.3 | 91.8 | 91.9 |
Table 3. Accuracy (%) of rule-presentation orders on Mixed-WM38.
| Method | C | D | E | L | R | S | Macro | Overall |
|---|---|---|---|---|---|---|---|---|
| Raw | 52.4 | 72.1 | 26.5 | 31.0 | 99.6 | 70.8 | 58.7 | 43.7 |
| Single | 94.7 | 61.6 | 79.1 | 78.4 | 69.8 | 76.4 | 76.7 | 80.1 |
| Multi | 91.3 | 73.1 | 81.0 | 76.4 | 74.8 | 80.9 | 79.1 | 81.3 |
Table 4. Accuracy (%) on WM811K.
Figure 4. Representative WM811K samples with weak or ambiguous class-specific morphologies; column headings denote source labels. Four representative WM811K wafer maps with weak or ambiguous class-specific morphologies. The source labels are Donut, Edge, Local, and Scratch. Colored overlays identify connected components rather than defect classes.
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