The circle fitting algorithm for wafer pre-aligners plays a pivotal role in wafer transmission technology. Traditional wafer pre-aligner adopts the method of one-time fitting after collecting complete data, resulting in low accuracy, high computational burden, and poor robustness. In this study, we propose an innovative online segmented fitting method based on a linear fitting model, which uses a recursive weighted least-squares (RWLS) algorithm to enhance the accuracy of fitting segmented data. To address computational complexities and improve algorithm robustness, we introduce the progressive sample consensus (PROSAC) algorithm, employing a uniform segmented strategy for rapid online selection of high-quality sampled data. Simulation experiments reveal that our method reduces total alignment time by 55.5% and decreases a mean fitting error by 78.8% compared to traditional one-time fitting methods. Furthermore, actual experimental results validate that our proposed method and algorithm not only reduce alignment time but also exhibit superior fitting accuracy and interference resistance.
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