Real-time control of reactive ion etching using neural networks

Stokes, D.; May, G.S.

doi:10.1109/acc.1997.610845

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Regarding selecting the transfer function in the hidden layer, Log-Sigmoid is one of the basic transfer functional types (11) An alternative type of transfer function is Hyper Tangent-Sigmoid (12) In this example, there are two hidden layers, and the possible numbers of nodes is between 1 and 50. Moreover, the possible transfer functions are Log-Sigmoid or Hyper Tangent-Sigmoid.…”

Section: A Construction Of Conjecture Modelmentioning

confidence: 98%

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A Processing Quality Prognostics Scheme for Plasma Sputtering in TFT-LCD Manufacturing

Hung

Cheng

et al. 2006

IEEE Trans. Semicond. Manufact.

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A novel quality prognostics scheme (QPS) for plasma sputtering in TFT-LCD manufacturing processes is proposed. The QPS consists of a conjecture model and a prediction model. The conjecture model can use processing parameters and sensor data to estimate the processing quality (sputtering thickness) in real time. This conjecture function is also called virtual metrology. On the other hand, the prediction model is capable of predicting the processing quality of the next-lot glasses. Neural networks and weighted moving average algorithms are applied to construct the QPS. In particular, a reliance index is developed such that online evaluation of whether the conjecture results of the QPS are trustworthy or not is possible. For increasing the accuracy of the QPS, a self-searching mechanism is designed to automatically search the best set of parameters and functions used by the conjecture and prediction algorithms for cases that the processing properties vary or the recipe changes. Also, an auto-adjusting mechanism is developed for tuning the system parameters of the QPS and bringing the conjecture accuracy within an acceptable bound. Thorough tests using normal and abnormal processing data on one set of plasma sputtering equipment in a TFT-LCD plant show that the values of the mean absolute percentage error of both the conjecture and the prediction results are less than 2%, which validates the effectiveness of the proposed QPS.Index Terms-Conjecture model, neural networks (NNs), prediction model, quality prognostics scheme (QPS), reliance index, virtual metrology.

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Section: A Construction Of Conjecture Modelmentioning

confidence: 98%

“…Hong et al [8] adopted principle component analysis [9], [10] and auto-encoder NNs to model reactive ion etching. Stokes and May [11] proposed the use of NNs for real-time model-based feedback control of reactive ion etching. Yi et al [12] developed an NN-based uniformity controller for linear chemical-mechanical planarization processes.…”

Section: Introductionmentioning

confidence: 99%

A Processing Quality Prognostics Scheme for Plasma Sputtering in TFT-LCD Manufacturing

Hung

Cheng

et al. 2006

IEEE Trans. Semicond. Manufact.

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“…In [10], a neural network model was introduced to study the plasma etching processes, and superior performance has been demonstrated compared with the SRM approach. Even though the neural network method has been applied to other semiconductor manufacturing processes such as CVD [11], [12], plasma and ion etchings [10], [13], [14], a small amount research has been performed for CMP processes. Lin and Liu [15] used an adaptive neuro-fuzzy interface system to analyze CMP process parameters on rotary CMP tools; however, very limited experiments and polishing parameters have been studied.…”

Section: Hemical-mechanical Planarization (Cmp)mentioning

confidence: 99%

Neural network based uniformity profile control of linear chemical-mechanical planarization

2003

IEEE Trans. Semicond. Manufact.

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In this paper, a neural network based uniformity controller is developed for the linear chemical-mechanical planarization (CMP) process. The control law utilizes the metrology measurements of the wafer uniformity profile and tunes the pressures of different air-bearing zones on Lam linear CMP polishers. A feedforward neural network is used to self-learn the CMP process model and a direct inverse control with neural network is utilized to regulate the process to the target. Simulation and experimental results are presented to illustrate the control system performance. Compared with the results by using statistical surface response methods (SRM), the proposed control system can give more accurate uniformity profiles and more flexibility.Index Terms-Chemical-mechanical planarization (CMP), neural networks, uniformity, run-to-run control.

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“…In a recognition application, the time delay neural network (TDNN) was proved to be successful for stable and robust real-time recognition [10]. The TDNN has also been widely used in the fields of communication, semiconductor engineering, control theory, and signal processing [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Time delay neural network modeling for particle size in SAG mills

Shang

2011

Powder Technology

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Real-time control of reactive ion etching using neural networks

Cited by 10 publications

References 23 publications

A Processing Quality Prognostics Scheme for Plasma Sputtering in TFT-LCD Manufacturing

A Processing Quality Prognostics Scheme for Plasma Sputtering in TFT-LCD Manufacturing

Neural network based uniformity profile control of linear chemical-mechanical planarization

Time delay neural network modeling for particle size in SAG mills

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