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	<title>wafer map defect classification &#8211; BIOENGINEER.ORG</title>
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		<title>Tiny AI Model Hits Near-Perfect Wafer Defect Detection on a Fab-Floor Budget</title>
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		<pubDate>Sat, 17 Oct 2026 17:00:49 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[CBAM attention module]]></category>
		<category><![CDATA[class imbalance]]></category>
		<category><![CDATA[convolutional autoencoder augmentation]]></category>
		<category><![CDATA[convolutional neural network]]></category>
		<category><![CDATA[edge AI]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[Grad-CAM]]></category>
		<category><![CDATA[Integrated Gradients]]></category>
		<category><![CDATA[lightweight deep learning]]></category>
		<category><![CDATA[semiconductor manufacturing]]></category>
		<category><![CDATA[wafer map defect classification]]></category>
		<category><![CDATA[WM-811K dataset]]></category>
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					<description><![CDATA[A 0.15-million-parameter attention-equipped neural network classifies nine wafer-map defect types with 99.88 percent accuracy on a CPU, combining latent-space augmentation with built-in explainability for real-time semiconductor inspection.]]></description>
		
		
		
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