Model-based control methods do not produce satisfactory control results with the batch process control of Czochralski (CZ) silicon monocrystalline with complex nonlinearity, large delay, and time-varying dynamics. Therefore, this paper proposes a data-driven model-free adaptive iterative learning control method (MFAILC) to achieve precision control of the batch process. Firstly, to improve the accuracy of the data-driven model, a novel deep learning model for crystal growth process is established by combining a stacked autoencoder (SAE) and a long short-term memory network (LSTM) to extract the working condition information and the dynamic timing features in process data. Traditional model-based control methods are limited by the difficulties in modeling and by the unmodeled dynamics. So, to overcome this problem, the heater controller and the crystal puller controller are designed, based on the iterative dynamic linearization technology, to ensure that the silicon monocrystalline batch manufacturing process always maintains precision control. Also, the discrete-time extended state observer (ESO) is introduced to compensate for the influence of disturbances, to improve the robustness of the control system. Finally, the efficacy of the proposed method is verified by applying it for the predictive modeling and batch control of thermal field temperature and crystal diameter during crystal growth.

The growth process of Czochralski (Cz) silicon single crystal is a dynamic time-varying system with nonlinearity, strong coupling, large hysteresis, and uncertain model. Traditional model-based control methods are difficult to achieve satisfactory crystal growth control effects, and it is difficult to ensure that the crystal quality meets the actual process requirements. Therefore, from the perspective of data-driven modeling and control, this paper proposes a model predictive control method for the crystal growth process with a V/G soft-sensing model for measuring crystal quality. First, because the V/G value to measure crystal quality is difficult to obtain directly, a hybrid variable weighted stacked autoencoder random forest (HVW-SAE-RF) soft-sensing model based on data-driven V/G value is established. Here, the HVW-SAE is used to extract the deep quality-related features of the measurable process data, and the RF is used for the regression prediction of the SAE output layer; Second, using the dual closed-loop control strategy, the inner loop is based on the HVW-SAE-RF soft-sensing model of the V/G value, and the predictive PI control method is used to control the V/G value closely related to the crystal quality within a reasonable range, and the outer loop based on real-time estimation of V/G values to achieve nonlinear model predictive control (NMPC) of crystal diameter; finally, the effectiveness of the proposed method is verified based on the industrial production process data of silicon single crystal.

The Czochralski (CZ) process is the core technology for producing semiconductor silicon monocrystalline (SMC), and it is a complex batch process. However, the crystal growth rate and crystal diameter, which are key quality indicators, are difficult to detect directly online, and the offline calculation process lags seriously, which easily causes blind crystal quality control. Therefore, this paper proposes a data-driven and mechanismbased hybrid model for semiconductor SMC quality variables prediction in the CZ process. Firstly, a data-driven model JITLSAE-ELM based on just-in-time learning (JITL) fine-tuning strategy is proposed. This model is used to solve the nonlinear and time-varying relationship of the energy transfer process in the CZ process that cannot be accurately described by traditional mechanism models. Here, the SAE-ELM model integrates a stacked autoencoder (SAE) and an extreme learning machine (ELM), which are used to deeply capture the nonlinear and timevarying features of process data. Secondly, according to the hydrodynamics and geometric behavior, a crystal pulling dynamic mechanism model based on the crystal growth mechanism is constructed, which avoids the complicated heat transfer link. Further, considering the unmodeled dynamics caused by model parameter uncertainty during the combination of the energy transfer model and crystal pulling dynamic mechanism model, a crystal diameter compensation model SAE-ELM was developed to improve the prediction accuracy of the CZ process hybrid model. Finally, an industrial data experiment based on a CZ monocrystal furnace illustrates the proposed method.

The growth process of silicon single crystal (SSC) is a typical batch production process, which has the characteristics of batch size, variety, complex process, and intensive technology. In order to accurately control the diameter and thermal field temperature of the Czochralski (Cz) SSC batch production process to ensure that the actual production requirements of electronic-grade SSC are met, this paper proposes an iterative learning-based batch process predictive control strategy Model Predictive Control -Iterative Learning Control -Extended State Observer (MPC-ILC -ESO). The control strategy consists of two parts. The time axis is a dual Model Predictive Control (MPC) controller, which is mainly used to deal with disturbance suppression and constraints in the production process of a single batch of SSC. The iterative axis is the Extended State Observer (ESO) based Iterative Learning Control (ILC) control algorithm to deal with uncertainty and disturbance estimation and compensation in the multi-batch production process. The control effects of the time axis and the batch axis are superimposed as the final controlled object input to ensure the crystal diameter and stable thermal field temperature. Finally, the effectiveness of the proposed control strategy is verified based on the data analysis of the semiconductor industry production process.

The accurate real-time prediction of the crystal quality index v/G is an important reference for the real-time monitoring of the growth quality status and the process optimization adjustment of semiconductor silicon single crystals. This paper proposes a data-driven crystal quality indicator v/G soft sensor prediction model based on multi-timescale feature fusion to achieve the effective prediction of the crystal quality indicator v/G. Firstly, the characteristics of the crystal quality index v/G in the growth process of Czochralski silicon single crystal are analyzed. Secondly, the crystal quality index v/G is broken down into several natural components using something called complete ensemble empirical mode decomposition with adaptive noise (CEEMDAD), which provides more stable data. On this basis, each intrinsic mode component is reconstructed according to the sample entropy. Then, the maximum mutual information coefficient (MIC) method is applied to identify the characteristic variables most closely associated with each reconstructed component of the crystal quality index v/G from the process-influencing factors. Then, a long short-term memory network with a self-attention mechanism is used to establish a prediction model of the reconstructed components to extract the multi-timescale feature information from the different components of the crystal quality index v/G. Finally, the prediction results of the crystal quality index v/G are obtained by fusing each subsequent prediction model. According to the actual field data, the comprehensive experimental results validate the efficacy of the proposed soft sensor modeling method for the crystal quality index v/G. Compared with the single model, the proposed prediction model has a smaller MAE, RMSE, and prediction performance index and a higher HR prediction hit rate.