(314g) Exploring Model-Based Control Design for Next-Generation Etch Cooling Systems

Etch cooling systems for the semiconductor manufacturing industry are critical to maintaining wafer etching performance. In particular, precise control of the wafer temperature is required to meet industrial specifications, which involve features such as shape and sharpness at modern technology nodes [1]. As described in [1] and [2], the next-generation etching process will require modules that may be incorporated into modern wafer temperature control (WTC) systems to operate in a wider range of operating conditions, including cryogenic process modules. To achieve this, the selection and exploration of advanced control designs for etch cooling systems under different operating strategies must be carefully considered [3].

Advances in measurements and process control of wafer temperature units have been reported in the literature (e.g., [4], [5], [6], [7], [8], [9]). Particularly, in [7], fitting polynomial models for statistical quality control have been used for a statistical feedback control framework that targets quality features (e.g., etch rate). In [8], a model-based control approach for etch processes has been proposed to reduce the critical dimension variations on the domain of across-wafer levels. In [9], a recurrent neural network-based model predictive controller (MPC) for a plasma etch process has been developed considering a multi-scale model to simulate the gas flows and chemical reactions of plasma and the etching process on the substrate. The process models developed for testing the control methods above were reduced-order models based on experimental data. In light of this, a modular closed-loop digital model for WTC systems may be used for data generation and further analysis of operating strategies, which could allow the semiconductor manufacturing industry to explore advanced control strategies in etching systems that do not need to rely on the actual physical system for testing novel control schemes while still achieving a high level of confidence.

Motivated by the above, prior work in our group [10] developed a tunable ANSYS/CFD model that captures the major features of typical cooling etch systems. The closed-loop system under PID controllers has been selected for the proposed ANSYS/CFD model, which represents a control strategy typically implemented in the semiconductor manufacturing industry. However, this control strategy using PID controllers may not work well for modern etch cooling systems, especially considering that fast and large set-point changes could happen to achieve desired industrial conditions and features in the wafer. To tackle this challenge, we discuss the process of designing an MPC for the WTC system, including considerations such as the impact of the MPC prediction horizon on control performance and computational complexity, timescale differences between the states of the system (e.g., flow rate changes over the pipes happen in a much faster timescale than temperature changes), state measurement availability, and MPC optimization problem formulation. In particular, as an MPC framework for the WTC system has beneficial properties compared to the WTC under PID controllers, the performance of both control systems will be compared. Specifically, MPC is an optimization-based control design, meaning that it computes optimal control actions to implement in the WTC system while accounting for hard process constraints and making predictions of how the WTC system will behave under the MPC control actions in the future (characteristics for which are not included in the PID controllers). Thus, the exploration of MPC strategies for cooling etch systems can provide flexibility for optimization and may enable new types of operating conditions to increase wafer etching performance. To illustrate this, a closed-loop description of the digital model developed in [10] for typical cooling etch systems under an MPC framework is discussed.

References

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