Modeling and Optimization of High Aspect Ratio Plasma Etching

Abstract

As critical dimensions of semiconductor devices shrink, feature densities increase and the geometries become more complex, the manufacturing processes are required to consistently improve and innovate. Low temperature plasma based processes are required to etch nanometer scale features with high aspect ratios through multi-material stacks as fast as possible, while maintaining high uniformity and a high yield over a 300 mm wafer. The quality of the surface etch is highly dependent on the energy and angular distribution of the charged particles, ions and electrons originating from the gas phase, incident on the wafer surface. To control their dynamics, the use of complex tailored voltage waveforms was investigated. The tailored waveform consisted of a sinusoidal harmonic wave and its higher harmonics. Coupled reactor and surface scale simulations were performed to investigate the respective physical regimes. The Hybrid Plasma Equipment Model (HPEM) was utilized to simulate the gas phase and discharge physics in a capacitively coupled plasma operated in the low-pressure regime. Investigated feed gas mixtures include Ar/O2, Ar/O2/CF4 as well as Ar/O2/C4F6. Based on the HPEM results, the Monte Carlo Feature Profile Model (MCFPM) was used to simulate the feature etch process into SiO2. It was found that some degree of control of charged particle dynamics is possible by adjusting the phase of higher harmonics φ through the resulting generation of electrical asymmetry and electric field reversal. These general trends were present in most considered configurations, however the nature of the interaction between ions and the generated DC self-bias were found to be context dependent with respect to its effects on ion energy. Two distinct regimes were identified. Average ion energy onto the wafer is strongly correlated to the DC self-bias at high f0, whereas in the low frequency regime this correlation is weak. Average ion energy onto the wafer is instead dominated by dynamic transients in the applied voltage waveforms. In all cases however, the trends produced in the gas phase translated to significant differences in the feature properties, strongly suggesting that voltage waveform tailoring constitutes a potent concept for etch process control. Additionally, as many other simulation concepts, the MCFPM is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface represented by the reaction probabilities, yields, rate coefficients, threshold energies etc. The increasing complexity of the structures being fabricated, new materials and novel gas compositions for plasma produced radical fluxes to the wafer also increases the complexity of the reaction mechanism used in feature scale models, and the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity or inadequate models. Methods to automate the selection of fundamental data in a reduced reaction mechanism for feature scale SiO2 plasma etching using a fluorocarbon gas mixture is discussed. By matching predictions of etch profiles to experimental data using a gradient descent / Nelder-Mead method hybrid optimization scheme these methods produce a reaction mechanism that replicate the experimental training data as well as experimental data using a related but different etch processes.