From Wafers to Electrodes: Transferring Automatic Optical Inspection (AOI) for Multiscale Characterization of Smart Battery Manufacturing

ABSTRACT The growing demand for scalable, reliable and cost‐effective battery manufacturing calls for fast, tight‐tolerance, and fully automatic characterization tools to understand battery materials. A major challenge lies in the characterization and control of electrode and cell‐level imperfections across the battery production chain. While laboratory‐based methods remain dominant in understanding material imperfections, their low throughput and high operational cost limit industrial scalability. Notably, automatic optical inspection (AOI) has been widely adopted in semiconductor manufacturing for inline inspection and control of imperfections. In this review, we discussed the critical challenges in transferring AOI techniques to battery manufacturing, which will bridge the knowledge gap between battery material characterization and semiconductor quality inspection. After discussing the imperfections and artificial intelligence (AI)‐driven optical techniques in the semiconductor industry, this review comprehensively assesses the electrode‐level and cell‐level imperfections in battery manufacturing. Offline and online battery characterization strategies, along with AI‐driven techniques in battery manufacturing, are discussed. We believe that digital twins (DTs), integrated with inline AOI and AI techniques, will have great potential for smart manufacturing of future batteries.