基于OES的等离子体刻蚀过程

光学发射光谱(OES)诊断技术是高密度等离子体刻蚀工艺过程的关键技术,OES信号作为一种实时信号可以用来预测刻蚀过程的进展程度和判断刻蚀性能的好坏.在自行研发的等离子体刻蚀机平台上,采用美国海洋公司的OES传感器系统,采集多晶硅刻蚀工艺中所产生的OES信号,利用主元素分析法(PCA)对OES数据进行压缩处理,提高了实时信号的快速处理能力.对实验数据的分析表明:波长为405 mn的OES谱线可以明确显示出等离子体刻蚀进程,是一条表征等离子体刻蚀过程的状态检测及终点检测控制的特征谱线.在此基础之上,提出了基于PCA法的终点检测算法,用以判断刻蚀终点.     

基于IEP的高密度等离子体刻蚀过程终点检测技术

高密度等离子体刻蚀是当今超大规模集成电路制造过程中的关键步骤.随着集成电路中器件尺寸的缩小及器件集成密度的提高,对刻蚀过程终点的准确判断是目前所面临的一个严峻考验,传统的OES终点检测技术已经远远不能满足深亚微米刻蚀工艺需求.讨论IEP终点检测技术的原理,针对多晶硅栅的等离子体刻蚀工艺,讨论了IEP终点检测技术在深亚微米刻蚀工艺中的应用,IEP预报式终点检测技术已经运用在新近研发的HDP刻蚀机的试验工艺上,最后对IEP终点检测技术未来的发展趋势进行了展望.     

高密度等离子体刻蚀机中的终点检测技术

高密度等离子体刻蚀是当今超大规模集成电路制造过程中的关键步骤.目前已经开发出许多终点检测技术.文章讨论了终点检测技术的原理,综述了目前主流刻蚀机使用的两种终点检测技术-OES和IEP-的最新进展,讨论了终点检测技术在深亚微米等离子体刻蚀工艺中的应用,以及所面临的挑战.     

用于实时刻蚀深度控制篚光干涉测量法

对等离子体刻蚀工艺过程中进行刻蚀的终点检测主要应用在以下两个方面：一是刻蚀穿透一种材料并在另一种材料的表面刻蚀过程终止．二是在一种材料内刻蚀到所需的深度后终止。在第一种应用中．由于刻蚀过程中不同材料的光发射谱是不一样．因而通常采用OES（光发射谱）方法进行终点检测。     

用于实时刻蚀深度控制的光干涉测量法

对等离子体刻蚀工艺过程中进行刻蚀的终点检测主要应用在以下两个方面:一是刻蚀穿透一种材料并在另一种材料的表面刻蚀过程终止,二是在一种材料内刻蚀到所需的深度后终止。在第一种应用中,由于刻蚀过程中不同材料的光发射谱是不一样,因而通常采用OES(光发射谱)方法进行终点检测,在刻蚀去除一种材料进入另一种材料时其光发射谱会产生变化,就可以用作工艺控制的终点检测。然而在第二种应用中,刻蚀过程中并不会出现光发射谱OES的变化,对刻蚀深度的控制通常由设定刻蚀工艺的时间来确定,但这种控制方法在实际工艺中很难达到高的控制精确度和可靠性。     

相变材料等离子体刻蚀的研究进展

首先综述了相变材料等离子体刻蚀技术的研究进展,然后讨论了影响相变材料等离子体刻蚀的主要工艺参数,如线圈功率、腔体气压、偏压、刻蚀气体及气体比例等,进而解释了工艺参数与刻蚀结果的依赖关系。同时采用多种分析手段,对相变材料在等离子体刻蚀工艺中产生的刻蚀损伤进行了分类和表征,并基于该分析结果提出了工艺优化方案。最后总结了相变材料等离子体刻蚀技术的反应机理,相变材料的刻蚀是自发反应与离子辅助化学反应相结合的过程,同时物理溅射与低挥发性产物的离子激发脱附也起着重要的辅助作用。     

高密度等离子刻蚀机中的等离子体诊断技术

等离子体诊断技术对于高密度等离子刻蚀过程的监控显得非常重要.讨论了几种主要的等离子体诊断技术:langmuir探针,发射光谱法(OES),激光诱导荧光法(LIF),光谱椭偏法,质谱法,并就其技术特点及在实际运用时面临的问题进行了详细的讨论.     

高纵横选择比低损伤多晶硅栅的工艺实现

分析了采用微波高密度等离子体刻蚀(HDP)系统刻蚀实现高纵横向刻蚀选择比、低等离子体损伤、精细线条尺寸的MOSFET多晶硅栅的可行性。研究了刻蚀用气体中CH4和SF6等离子体分别在多晶硅栅刻蚀当中的作用及其分别对刻蚀速率、多晶硅栅侧壁形貌的影响原理。提出了实现MOSFET多晶硅栅高速低损伤刻蚀及聚合物清洗相结合的两步刻蚀工艺技术。借助终点检测技术(EPD),通过优化各气体体积流量及合理选择两步刻蚀时间较好实现了较高的纵横向选择比、低刻蚀损伤及精细线条的MOSFET多晶硅栅刻蚀。     

干法刻蚀InSb时Ar气含量的选取

感应耦合等离子体(ICP)刻蚀技术是目前国际制备InSb台面结型焦平面阵列技术的主流技术之一。文章研究采用ICP刻蚀技术,以CH4/H2/Ar刻蚀气体体系对InSb材料进行干法刻蚀时刻蚀气体体系中Ar气体积组分对材料刻蚀形貌及器件性能的影响。实验方案设置为在控制其他实验变量相同的情况下设置不同Ar气体积分数的刻蚀气体体系对InSb材料进行台面蚀刻,通过扫描电子显微镜(SEM)、激光共聚焦显微镜考察台面刻蚀形貌,通过I-V测试得到的器件性能结果考察刻蚀损伤情况。从实验结果得到,Ar体积占据总气体体积的30%～35%时台面刻蚀形貌良好,表面损伤轻,器件性能良好,刻蚀工艺满足要求。     

高密度RF等离子体刻蚀工艺直流自我偏压的研究

直流自我偏压作为高密度射频(RF)等离子体刻蚀工艺中的重要电学参数,反映出具有高能量的离子对待刻蚀晶片的轰击效果,后者决定了刻蚀工艺的各向异性、刻蚀速率、选择比及形貌特征等工艺结果。该文以HBr作为刻蚀气体,采用电感耦合等离子体(ICP)金属刻蚀系统针对刻蚀工艺中的直流自我偏压进行研究。研究中分别改变离子源功率、衬底偏压功率、刻蚀压力及HBr气体流量,观察直流自我偏压及其峰值的相应变化规律。实验结果表明,随着离子源功率的升高,直流自我偏压将会轻度降低;升高偏压功率则会显著提升直流自我偏压。刻蚀压力与直流自我偏压呈正比例关系,HBr气体流量的变化及待刻蚀晶片的材质对直流自我偏压无显著影响。     

Low open-area endpoint detection using a PCA-based T2statistic and Q statistic on optical emission spectroscopy measurements

Examines an approach for automatically identifying endpoint (the completion in etch of a thin film) during plasma etching of low open area wafers. Because many end-pointing techniques use a few manually selected wavelengths or simply time the etch, the resulting endpoint detection determination may only be valid for a very short number of runs before process drift and noise render them ineffective. Only recently have researchers begun to examine methods to automatically select and weight spectral channels for estimation and diagnosis of process behavior. This paper will explore the use of principal component analysis (PCA)-based T² formulation to filter out noisy spectral channels and characterize spectral variation of optical emission spectroscopy (OES) correlated with endpoint. This approach is applied and demonstrated for patterned contact and via etching using digital semiconductor's CMOS6 (0.35-μm) production process     

Plasma impedance monitoring for real time endpoint detection of bulk materials etched in ICP tool

Plasma impedance monitoring (PIM) based on electrical measurements is successfully used as an alternative to determine real time detection endpoint during plasma etching of structured bulk materials. In this paper we present the results with this technique for the endpoint detection during the etching of various materials.The endpoint conditions are tested in the sixth harmonic components of the electrical plasma parameters with an RF sensor. The endpoint is determined when an electrical parameter transition is observed. This transition corresponds to the change of the total reactor impedance, and allows the etching of the doped layer to stop on the bulk substrate.Using a Smith chart we determine the best harmonics/electrical monitoring couple parameters for processes on various materials. Resistivity measurements are used before and after etching in order to confirm the usefulness of the PIM method.In this paper, we also demonstrate how to monitor a real time control of non-uniformity during the reactive ion etching (RIE) process in the case of gallium arsenide etching.     

Modeling and algorithm development for automated optical endpointing of an HBT emitter etch

This paper discusses the development of a high-accuracy endpointing algorithm for the emitter etch of a heterojunction bipolar transistor (HBT). Fabrication of high-performance HBTs using self-aligned base-emitter processes requires etching through the emitter layer and stopping with very high accuracy on the base layer. The lack of selectivity in dry etching coupled with the high etch rates possible in high density plasmas render the use of a standard timed overetch impractical, especially as device layers continue to become thinner. The etch process under study requires the complete removal of an AlInAs emitter while etching no more than 5 nm of the underlying GaInAs base layer. Etch products are monitored using optical emission spectroscopy (OES) to determine etch endpoint. The process under study relies on the intensity of the 417.2 nm Ga emission line. The detection of the Ga line indicates that the etch has reached the GaInAs layer. However, the presence of a time-varying Ga baseline signal before endpoint and significant noise in the OES signal necessitate more than a simple threshold scheme for critical endpoint detection. The algorithm presented here is based on a generalized likelihood ratio with a signature function. This algorithm is robust to variance in the optical gains of the measurement equipment and is applicable to other etch processes. Experimental results of automated endpointing using this algorithm are presented in the form of pre- and post-etch ex situ film thickness measurements.     

Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching

To achieve timely and accurate fault detection in reactive ion etching, neural networks (NNs) have been applied for the fusion of data generated by two in-situ sensors: optical emission spectroscopy (OES) and residual gas analysis (RGA). While etching is performed, OES and RGA data are simultaneously collected in real time. Several pre-determined, statistically significant wavelengths (for OES data) and atomic masses (for RGA signals) are monitored. These data are subsequently used for training NN-based time series models of process behavior. Such models, referred to herein as time series NNs (TSNNs), are realized using multilayered perceptron NNs. Results indicate that the TSNNs not only predict process parameters of interest, but also efficiently perform as sensor fusion of the in-situ sensor data.     

非晶硅基薄膜的等离子体刻蚀光发射谱检测

本文研究了采用锁定放大相干检测技术的等离子体光发射谱检测系统。用该系统检测了仅用CF4作为刻蚀气体刻蚀非晶硅基薄膜的等离子体光发射谱。分析了检测结果和刻蚀机理。     

Fault detection of plasma etchers using optical emission spectra

The objective of this paper is to investigate the suitability of using optical emission spectroscopy (OES) for the fault detection and classification of plasma etchers. The OES sensor system used in this study can collect spectra at up to 512 different wavelengths. Multiple scans of the spectra are taken from a wafer, and the spectra data are available for multiple wafers. As a result, the amount of the OES data is typically large. This poses a difficulty in extracting relevant information for fault detection and classification. In this paper, we propose the use of multiway principal component analysis (PCA) to analyze the sensitivity of the multiple scans within a wafer with respect to typical faults such as etch stop, which is a fault that occurs when the polymer deposition rate is larger than the etch rate. Several PCA-based schemes are tested for the purpose of fault detection and wavelength selection. A sphere criterion is proposed for wavelength selection and compared with an existing method in the literature. To construct the final monitoring model, the OES data of selected wavelengths are properly scaled to calculate fault detection indices. Reduction in the number of wavelengths implies reduced cost for implementing the fault detection system. All experiments are conducted on an Applied Materials 5300 oxide etcher at Advanced Micro Devices (AMD) in Austin, TX     

基于ICP的硅高深宽比沟槽刻蚀技术

介绍了电感耦合等离子体(ICP)刻蚀技术的基本概念。结合英国STS公司的STS multiplex ICP system刻蚀机,介绍了刻蚀机原理及刻蚀过程。对硅深槽刻蚀技术进行了分析,对其中Footing效应、Lag效应和侧壁光滑问题提出了优化方案,最后在实验的基础上得出了能够刻蚀出高质量硅深沟槽的刻蚀参数。     

Neural network modeling of reactive ion etching using optical emission spectroscopy data

Neural networks are employed to model reactive ion etching (RIE) using optical emission spectroscopy (OES) data. While OES is an excellent tool for monitoring plasma emission intensity, a primary issue with its use is the large dimensionality of the spectroscopic data. To alleviate this concern, principal component analysis (PCA) and autoencoder neural networks (AENNs) are implemented as mechanisms for feature extraction to reduce the dimensionality of the OES data. OES data are generated from a 2/sup 4/ factorial experiment designed to characterize RIE process variation during the etching of benzocyclobutene (BCB) in a SF/sub 6//O/sub 2/ plasma, with controllable input factors consisting of the two gas flows, RF power, and chamber pressure. The OES data, consisting of 226 wavelengths sampled every 20 s, are compressed into five principal components using PCA and seven features using AENNs. Each method is subsequently used to establish multilayer perceptron neural networks trained using error back-propagation to model etch rate, uniformity, selectivity, and anisotropy. The neural network models of the etch responses using both methods show excellent agreement, with root-mean-squared errors as low as 0.215% between model predictions and measured data.