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To elucidate the wear mechanisms of diamond AFM tips during nanoscale scribing of single-crystal silicon, this study combines controlled experiments with atomistic molecular dynamics (MD) simulations. Scribing tests were conducted under systematically varied bias current, scribing speed, and scribing distance. Tip morphology evolution was quantitatively characterized. Concurrently, a three-dimensional MD model reproduced probe–silicon interactions to analyze bond breaking, atomic detachment, and structural transformation at the atomic scale. The results show that increasing current, speed, and distance significantly accelerate tip blunting. Simulations reveal a progressive transition in deformation behavior from elastic response to atomic attrition, plastic damage, brittle cracking, and catastrophic fracture as indentation depth increases, and cluster analysis establishes a quantitative correlation between process parameters and wear severity. This integrated experimental simulation framework provides mechanistic insight into diamond tip degradation and offers quantitative guidance for improving probe durability and process reliability in AFM-based nanofabrication.

Present study employes a picosecond laser (532 nm) for selective P1 laser scribing on the indium tin oxide (ITO) layer and subsequent fine‐tuning of P1 scribing conditions with machine learning (ML) techniques. Initially, the scribing is performed by varying different laser parameters and further evaluate them via an optical microscope and two probe resistivity measurements. The corresponding scribing width and sheet resistance data are used as input databases for ML analysis. The classification and regression tree (CART)‐based ML analysis revealed that median pulse energy <5.7 μJ insufficient to separate the adjacent scribing regions. While pulse energy >5.7 μJ, APL > 35%, LSO > 46%, and processing speed ≥1250 mm s−1 gives ≥16 μm of scribing width. Further, the decision tree (DT) analysis showed that pulse energy of ≥8.1 μJ, and LSO ≥ 37% are required for electrically isolated lines. The feature importance score suggests that laser fluence and pulse energy determined the scribing width, whereas electrical isolation strongly depends on LSO and processing speed. Finally, the ML achieved conditions experimentally validated and reassessed via scanning electron microscope, and atomic force microscopy aligns well with optical microscope measurements. The present work demonstrates the machine learning (ML)‐assisted efficient optimization of the P1 laser scribing process condition over indium tin oxide (ITO) substrate with a picosecond laser and its experimental validation. This approach will be helpful in different photovoltaic (PV) technologies to make modules, where transparent conducting oxide substrates are widely used.