Reactive ion etch modeling using neural networks and simulated annealing

Silicon dioxide films are useful as interlayer dielectrics for integrated circuits and multichip modules (MCM´s), and reactive ion etching (RIE) in RF glow discharges is a popular method for forming via holes in SiO₂ between metal layers of an MCM. However, precise modeling of RIE is difficult due to the extremely complex nature of particle dynamics within a plasma. Recently, empirical RIE models derived from neural networks have been shown to offer advantages in both accuracy and robustness over more traditional statistical approaches. In this paper, a new learning rule for training back-propagation neural networks is introduced and compared to the standard generalized delta rule. This new rule quantifies network memory during training and reduces network disorder gradually over time using an approach similar to simulated annealing. The modified neural networks are used to build models of etch rate, anisotropy, uniformity, and selectivity for SiO₂ films etched in a chloroform and oxygen plasma. Network training data was obtained from a 2⁴ factorial experiment designed to characterize etch variation with RF power, pressure, and gas composition. Etching took place in a Plasma Therm 700 series RIE system. Excellent agreement between model predictions and measured data was obtained