  To support the ever-increasing performance requirements of lithography
systems in terms of throughput and accuracy, in this paper, we introduce a
design framework for partial state-feedback reduced-order switching predictive
models. By combining measurements and predictions, this method aims to: 1)
improve overall system performance by reducing the placement errors in a die
within and across the full-wafer and 2) eliminate redundant measurements by
using predictions to improve system throughput. We primarily focus on
well-known trade-off introduced by measurement time, which can correct errors
at a cost of throughput, noise and not being robust to uncertainties. The
proposed approach consists of a reduced-order model with a switching logic that
acts a scheduler to deal with uncertain operating conditions. It also utilizes
linear predictive models as a basis for the control design which is appealing
to the ease and cost of implementation, enhancing the applicability. For the
add-on part, the scheduler logic is adapted based on expected operating
conditions of the system while guaranteeing global uniform ultimate bounded
asymptotic stability. Lastly, to deal with measurement layouts, the predictor
combines the measurement into model using partial state-feedback. Effectiveness
of the proposed strategy is demonstrated in practice on a high-precision
industrial scanner.
