Autonomous AI Assistant for Semiconductor Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption in Low-Resource Settings
Keywords: Semiconductor Imaging and Analysis, Vision-Language Instruction Tuning, Autonomous Small-Scale MultiModal Assistants
Abstract: Semiconductor imaging and analysis are critical yet understudied in deep learning, limiting our ability for precise control and optimization in semiconductor manufacturing. We introduce a small-scale multimodal framework for analyzing semiconductor electron microscopy images (MAEMI) through vision-language instruction tuning. We generate a customized instruction-following dataset using large
multimodal models on microscopic image analysis. We perform knowledge transfer from larger to smaller models through knowledge distillation, resulting in improved accuracy of smaller models on visual question answering (VQA) tasks. This approach eliminates the need for expensive, human expert-annotated datasets for microscopic image analysis tasks. MAEMI, can assist, accelerate, and even automate the semiconductor electron microscopy image ananysis tasks. Enterprises can further fine-tune MAEMI on their intellectual data, enhancing privacy and performance on low-cost consumer hardware. Our experiments show that MAEMI outperforms traditional methods, adapts to data distribution shifts, and supports high-throughput screening.
Submission Number: 1
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