【摘要】：硅單晶作為半導體行業(yè)基礎(chǔ)材料之一,對集成電路產(chǎn)業(yè)發(fā)展起著重要作用。隨著極大規(guī)模集成電路發(fā)展,對硅單晶提出了大尺寸、高品質(zhì)等要求。目前制備硅單晶最為重要的方法是基于傳統(tǒng)控制結(jié)構(gòu)的直拉法。在傳統(tǒng)控制結(jié)構(gòu)中,目標溫度跟蹤曲線依賴人工經(jīng)驗且控制器參數(shù)常通過反復實驗獲得。除此之外,熱場溫度控制晶體直徑環(huán)節(jié)存在非線性、大時滯、緩時變特性。為避免目標溫度跟蹤曲線和控制器參數(shù)設(shè)置不合理,最有效策略是辨識熱場溫度-晶體直徑非線性動態(tài)模型,并采用基于模型的控制策略實現(xiàn)晶體直徑控制,從而達到提高晶體品質(zhì)的目的。本文在硅單晶生長原理及傳統(tǒng)硅單晶控制結(jié)構(gòu)研究基礎(chǔ)上,提出一種恒拉速熱場溫度-晶體直徑辨識和控制方案。利用等徑階段的熱場溫度和晶體直徑數(shù)據(jù)及輸出相關(guān)性時滯確定算法獲得熱場溫度-晶體直徑非線性動態(tài)模型時滯;采用利普希茨商確定模型輸入輸出階次;在模型參數(shù)辨識后采用增強型相關(guān)檢驗算法優(yōu)化模型階次并對辨識模型進行檢驗;參數(shù)辨識分別基于動態(tài)BP神經(jīng)網(wǎng)絡(luò)和棧式稀疏自動編碼器實現(xiàn)。獲得有效熱場溫度-晶體直徑模型后,分別采用基于動態(tài)BP神經(jīng)網(wǎng)絡(luò)和基于棧式稀疏自動編碼器預測模型的廣義預測控制算法對晶體直徑進行控制仿真實驗。實驗結(jié)果表明:提出的辨識方案可以有效獲得熱場溫度-晶體直徑大時滯非線性動態(tài)模型;將棧式稀疏自動編碼器引入廣義預測控制能夠有效跟蹤設(shè)定晶體直徑,但該算法在線修正實時性較差。基于動態(tài)BP神經(jīng)網(wǎng)絡(luò)的廣義預測控制算法不僅能夠準確跟蹤設(shè)定晶體直徑,同時能夠通過在線學習對預測模型予以修正,以適應(yīng)系統(tǒng)緩時變特征。
[Abstract]:Silicon single crystal, as one of the basic materials in semiconductor industry, plays an important role in the development of integrated circuit industry. With the development of very large scale integrated circuits, large size and high quality requirements have been put forward for silicon single crystals. At present, the most important method to prepare silicon single crystal is Czochralski method based on traditional control structure. In the traditional control structure, the target temperature tracking curve depends on artificial experience and the controller parameters are often obtained by repeated experiments. In addition, the diameter link of thermal field temperature control crystal has nonlinear, large time delay and slow time-varying characteristics. In order to avoid unreasonable setting of target temperature tracking curve and controller parameters, the most effective strategy is to identify the nonlinear dynamic model of temperature-crystal diameter in thermal field, and to realize crystal diameter control by using model-based control strategy. So as to achieve the purpose of improving the crystal quality. Based on the growth principle of silicon single crystal and the control structure of traditional silicon single crystal, a temperature-crystal diameter identification and control scheme for constant pull speed thermal field is proposed in this paper. The nonlinear dynamic model delay of thermal field temperature and crystal diameter is obtained by using the thermal field temperature and crystal diameter data of equal diameter stage and the time delay determination algorithm of output correlation, and the input and output order of the model is determined by Lipschitz quotient. After the model parameter identification, the enhanced correlation test algorithm is used to optimize the model order and test the identification model, and the parameter identification is realized based on dynamic BP neural network and stack sparse automatic encoder, respectively. After the effective thermal field temperature-crystal diameter model is obtained, the generalized predictive control algorithm based on dynamic BP neural network and stack sparse automatic encoder prediction model is used to simulate the crystal diameter control. The experimental results show that the proposed identification scheme can effectively obtain the nonlinear dynamic model of thermal field temperature-crystal diameter with large time delay. The introduction of stack sparse automatic encoder into generalized predictive control can effectively track the set crystal diameter, but the real-time performance of the algorithm is poor. The generalized predictive control algorithm based on dynamic BP neural network can not only accurately track the set crystal diameter, but also modify the prediction model through online learning to adapt to the slow time-varying characteristics of the system.
【學位授予單位】：西安理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN304.12

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本文編號：2494716