Plasma information–based characterization of wedge-shaped bowed etching profile driven by ion dynamics with the tailored voltage waveform bias

Wedge-shaped bowing during high-aspect-ratio Si trench etching under tailored voltage waveform (TVW)-driven dual-frequency capacitively coupled plasma conditions is analyzed from a plasma physics perspective. TVW operation modifies RF sheath dynamics, producing a bi-modal ion energy distribution with an enhanced high-energy component. When reflected from tapered SiO2 mask sidewalls, these ions induce localized energy-flux focusing near the upper trench region, resulting in sharp wedge-shaped profile deformation. Combining in situ diagnostics with feature-scale transport modeling, a plasma-information index, PIBow, is introduced by normalizing the ratio of reflected high-energy ion energy flux to transported O radical flux to a characteristic profile-transition reference scale. This dimensionless index quantifies the competition between ion-driven etching and radical-mediated passivation, and wedge-forming and non-wedge-forming conditions are systematically organized around the normalized reference state PIBow≈1 for the analyzed system. Furthermore, a plasma information–based virtual metrology framework then transforms optical emission spectroscopy and voltage-current sensor measurements into physics-informed plasma variables for wedge geometry prediction. The model reproduces the measured wedge length with a high coefficient of determination (adj. R2 = 0.981) and yields an interpretable process-control window governed by the balance between energetic-ion delivery and O-radical-mediated passivation. An angle-dependent etch rate model is also proposed to describe wedge angle formation, reproducing the measured dependence on the operating conditions. Wedge-shaped bowing thus represents a distinct regime of profile evolution governed by coupled ion dynamics and transport-limited radical supply under TVW conditions, and the methodology presented here offers a scalable framework for understanding, predicting, and engineering plasma–surface interactions in plasma-assisted manufacturing.