The tip features can be estimated by this model with a correlation factor R over 0.85 indicating great predictive performance. Garson’s algorithm is used to analyze the relative importance of each experimental parameter on tip features. Furthermore, in order to find the relationship between the tip features and experimental etching parameters, an artificial neural network (ANN) model is built by machine learning. These tips are capable of producing high-quality atomic resolution STM images, as demonstrated by testing on Si (111) and highly oriented pyrolytic graphite (HOPG) samples at room temperature. Under these conditions, the smallest tip radius is around 8 nm and the yield (radius < 10 nm) is 63.5%. In this work, significant enhancing of the yield is presented through optimized etching parameters as follows: temperature of 26 ☌, applied voltage 7.5 V, electrolyte concentration 4 mol L −1 and length below the liquid lamellae of 2 mm. However, it is still challenging for this method to get ultra-sharp tungsten tips with high yield. The double-electrolyte etching method is a simple and effective way to fabricate ultra-sharp scanning tunnel microscopy (STM) tungsten tips.
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