This paper presents results and commentary on using a cascade neural network and a policy-iteration optimization routine to provide suggested process setpoints for recovery from long-term machine drift in a LAM 4520 6-in dielectric etcher. Traditional plasma etch variables such as pressures, gas flows, temperatures, RF power, etc. are combined with a generalized representation of the time dependent effects of maintenance events to predict film etch rates, uniformity, and selectivity. A cascade neural-network model is developed using 15 months of data divided into train, test, and validation sets. The neural model both fits the validation data well and captures the nonuniformity in the in-control region of the machine. Two control algorithms use this model in a predictive configuration to identify input state changes, including maintenance events, to bring an out-of-control situation back into control. The overall goal of the optimization is to reduce equipment downtime and decrease cost of ownership of the tool by speeding up response time and extending the lifetime of consumable parts. The optimization routines were tested on 11 out-of-control situations and successfully suggested reasonable low-cost solutions to each for bringing the system back into control.
