Gallium arsenide surface chemistry and surface damage in a chlorine high density plasma etch process

In an effort to monitor ion-driven surface chemistry in the high density plasma etching of GaAs by Cl₂/Ar plasma chemistries, we have applied mass spectrometry and careful substrate temperature control. Etch product chlorides were mass analyzed while the substrate temperature was monitored by optical bandgap thermometry and as pressure (neutral flux), microwave power (ion flux) and rf bias of the substrate (ion energy) were varied. By ensuring that the substrate temperature does not deviate during process variations, the changes in product mass peak intensities are a direct measure of changes in the ionassisted surface chemistry which promotes anisotropic etching. Experimental results show that ion-assisted surface chemistry is optimum when sufficient Cl and Cl⁺ are present in the incident plasma flux. These conditions are met at low coupled microwave powers (<300 W) and low total process pressures (<1.0 mTorr) for input gas mixtures of 25% Cl₂ in Ar. Three mechanistic regions are identified for surface chemistry as a function of incident ion energy: 1) largely thermal chemistry for <50 eV; 2) ion-assisted chemistry for 50–200 eV; and 3) sputtering for >200 eV. Photoreflectance measurements of the surface Fermi level show significant damage for ion energies >75 eV. However, in situ and ex situ surface passivations can recover the surface Fermi level for up to 200 eV ion energies, in good correlation to the onset of sputtering and subsurface damage. Thus, anisotropic, low damage pattern transfer is possible for ion energies between 50 and 200 eV.     

Statistical feedback control of a plasma etch process

This paper presents the methodology developed for the automatic feedback control of a silicon nitride plasma etch process. The methodology provides an augmented level of control for semiconductor manufacturing processes, to the level that the operator inputs the required process quality characteristics (e.g. etch rate and uniformity values) instead of the desired process conditions (e.g., specific RF power, pressure, gas flows). The optimal equipment settings are determined from previously generated process/equipment models. The control algorithm is driven by the in-situ measurements, using in-line sensors monitoring each wafer. The sensor data is subjected to Statistical Quality Control (SQC) to determine if deviations from the required process observable values can be attributed to noise in the system or are due to a sustained anomalous behavior of the equipment. Once a change in equipment behavior is detected, the process/equipment models are adjusted to match the new state of the equipment. The updated models are used to run subsequent wafers until a new SQC failure is observed. The algorithms developed have been implemented and tested, and are currently being used to control the etching of wafers under standard manufacturing conditions     

Contact etch process optimization for RF process wafer edge yield improvement

Radio-frequency (RF) process products suffer from a wafer edge low yield issue, which is induced by contact opening. A failure mechanism has been proposed that is based on the characteristics of a wafer edge film stack. The large step height at the wafer’s edge leads to worse planarization for the sparse poly-pattern region during the inter-layer dielectric (ILD) chemical mechanical polishing (CMP) process. A thicker bottom anti-reflect coating (BARC) layer was introduced for a sparse poly-pattern at the wafer edge region. The contact open issue was solved by increasing the break through (BT) time to get a large enough window. Well profile and resistance uniformity were obtained by contact etch recipe optimization.     

A neural network model of a contact plasma etch process for VLSIproduction

The etch process for preparation of via contacts in VLSI manufacturing is described along with a neural network model of the process. The neural network is a two hidden layer network (23-3-3-1) trained by error back-propagation. The input variables to the model are the mean values of set-point fluctuations for the control variables of the plasma reactor, and the output is the oxide thickness remaining after the etch. The model is thus abstracted by several levels of reality. The real-world process results in a film thickness about 24 000 Å and a standard deviation of about 730 Å. We demonstrate that a neural network model can predict the post-etch oxide thickness to within 480 Å and that inherent noise in the training/testing data is 416 Å. We also demonstrate that the dc bias and the etch times are the most important variables to determine the final product quality     

Fault detection for a via etch process using adaptive multivariate methods

Multivariate process control charts like Hotelling T/sup 2/ and squared prediction error are gaining acceptance in the semiconductor industry to monitor the increasing amount of data available by modern process tools. These methods require models built based on the covariance matrix of a trainings data set. Slowly drifting manufacturing processes degrade this estimation for the covariance matrix creating false alarms. To overcome the problem, adaptive modeling schemes are considered. The tradeoff between sensitivity and false alarms for static and adaptive models applied to a via etching process is demonstrated. Possible improvements by incorporating domain knowledge are shown.     

In situ etch treatment of bulk surface for epitaxial layer growth optimization

Homoepitaxial bulk 4H SiC-off-axis commercial wafers were investigated after in situ hydrogen etching on a hot wall chemical vapor deposition (HWCVD).We have performed test etching on several process conditions in order to study the surface defects reduction or transformation. A detailed map of bulk defects has been obtained by optical microscopy inspection to mark interesting position of investigated area and to identify the same area after chemical etching, with the aim to compare the defect evolution after hydrogen etching in the reactor. The highlighted defects area was analysed by means of atomic force microscopy and Micro Raman spectroscopy in order to obtain morphological and structural information. On the etched surface bulk wafer a epilayer was grown by HWCVD reactor to study the development of marked defects.The etched surfaces show a significant defect density reduction and present a good surface morphology.     

Advantages of plasma etch modeling using neural networks overstatistical techniques

Due to the inherent complexity of the plasma etch process, approaches to modeling this critical integrated circuit fabrication step have met with varying degrees of success. Recently, a new adaptive learning approach involving neural networks has been applied to the modeling of polysilicon film growth by low-pressure chemical vapor deposition (LPCVD). In this paper, neural network modeling is applied to the removal of polysilicon films by plasma etching. The plasma etch process under investigation was previously modeled using the empirical response surface approach. However, in comparing neural network methods with the statistical techniques, it is shown that the neural network models exhibit superior accuracy and require fewer training experiments. Furthermore, the results of this study indicate that the predictive capabilities of the neural models are superior to that of their statistical counterparts for the same experimental data     

Abnormal metal oxide formation induced by residual charging in plasma etch process

Electron cyclotron resonance plasma with SF₆ and Cl₂ gas mixture were used for tungsten plug etch-back processes. The properties of electric contacts between tungsten plugs and Al/Ti/TiN interconnect lines, fabricated by this etching process, have been studied. Particles and abnormal oxide layers at the plug/line interfaces have been found to be the main factor to cause deterioration of the electric contacts. Mechanisms for particle transportation and metal oxide formation have been proposed. The phenomenon was attributed to the residual charging effect, which occurred immediately after the plasma power being turned off. A technique to prevent the residual charging induced tungsten oxide growth has been developed and applied in industrial fabrication lines.     

Minimum geometry etch windows to a polysilicon surface

Windows of less than 0.1 µm in width on a polysilicon surface are demonstrated. These narrow windows have been produced by the edge etch process. The usefulness of this technique for producing narrow openings in a polysilicon layer is discussed.     

Simulation and experimental analysis of planarization of refractory metals using a multi-step sputter/sputter etch process

In this paper, we will present both experimental and simulated results showing the use of successive sputter deposition and sputter etching of refractory metal films deposited into trenches. A layer of 0.5 μm thick film was deposited using a magnetron sputtering source and then etched back to 0.25 μm using radio frequency sputter etching. A multilayer film was built up by repetition of this process. Both the step coverage and planarization of the films improved relative to one-step sputter deposited films. The SIMulation by BAllistic Deposition (SIMBAD) simulation program was extended to allow for the modeling of multilayer films. SIMBAD was successful in modeling the sputter/sputter etch process and accurately modeled the microstructure of the films deposited. Particular emphasis in the paper is put on evaluating the performance of SIMBAD in predicting the microstructure of the film. This microstructure was found to be substantially altered if the vacuum system was vented between layer deposition.     

Monitoring and diagnosis of plasma etch processes

Plasma etching removes material from a silicon wafer by applying power and gases in a chamber. As material is removed from a wafer, the amount of particular chemicals given off can be measured; this technique is called emission spectroscopy and the measurements are called endpoint traces. An expert system that automatically interprets the traces has been designed and built. The system combines signal-to-symbol transformations for data abstraction and rule-based reasoning for diagnosis. The system detects problems as soon as they occur and also determines their causes     

Automated malfunction diagnosis of semiconductor fabricationequipment: a plasma etch application

A general methodology for the automated diagnosis of integrated circuit fabrication equipment is presented. The technique combines the best aspects of quantitative algorithmic diagnosis and qualitative knowledge-based approaches. Evidence from equipment maintenance history, real-time tool data, and incline measurements are integrated using evidential reasoning. This methodology is applied to the identification of faults in the Lam Research Autoetch 490 automated plasma etching system located in the Berkeley Microfabrication Laboratory     

Measurement of plasma etch damage by a new slow trap profilingtechnique

MOS capacitor structures with plasma damaged oxides have been used to demonstrate a new technique for profiling slow traps at the Si-SiO ₂ interface. The technique measures the density and trapping rate of slow traps by stepping the gate voltage in small increments and monitoring the resulting substrate current transients, thereby producing a profile of the traps in energy and response time. The response time is a function of the trap's energy position and distance from the interface. Some traps created by plasma etching are not obvious in quasistatic CV measurements, yet are clearly evident when the new technique is used. Results show an increase in slow trap densities and response times in the upper half of the silicon bandgap with long plasma overetch times. In comparison, wet etched control devices show only low densities of slow traps with shorter response times around the midgap     

Spatial characterization of wafer state using principal componentanalysis of optical emission spectra in plasma etch

Optical emission spectroscopy (OES) is often used to obtain in-situ estimates of process parameters and conditions in plasma etch processes. Two barriers must be overcome to enable the use of such information for real-time process diagnosis and control. The first barrier is the large number of measurements in wide-spectrum scans, which hinders real-time processing. The second barrier is the need to understand and estimate not only process conditions, but also what is happening on the surface of wafer, particularly the spatial uniformity of the etch. This paper presents a diagnostic method that utilizes multivariable OES data collected during plasma etch to estimate spatial asymmetries in commercially available reactor technology. Key elements of this method are: first, the use of principal component analysis (PCA) for dimensionality reduction, and second, regression and function approximation to correlate observed spatial wafer information (i.e., line width reduction) with these reduced measurements. Here we compare principal component regression (PCR), partial least squares (PLS), and principal components combined with multilayer perceptron neural networks (PCA/MLP) for this in-situ estimation of spatial uniformity. This approach has been verified for a 0.35-μm aluminum etch process using a Lam 9600 TCP etcher. Models of metal line width reduction across the wafer are constructed and compared: the root mean square prediction errors on a test set withheld from training are 0.0134 μm for PCR, 0.014 μm for PLS, and 0.016 μm for PCA/MLP. These results demonstrate that in-situ spatially resolved OES in conjunction with principal component analysis and linear or nonlinear function approximation can be effective in predicting important product characteristics across the wafer     

Internal linear inductively coupled plasma (ICP) sources for large area FPD etch process applications

A large area (830 mm×1020 mm) inductively coupled plasma source with a six internal straight antennas was developed for large area FPD (Flat Panel Display) etch process applications and the effects of magnetic fields employing permanent magnets on the plasma characteristics were investigated. Using six straight antennas connected in series into plasma and though the induction of strong electric field into the plasma by the antennas, high-density plasma on the order of 10¹¹ cm⁻³ could be obtained by applying above 1500 W power to the antennas. By employing the magnetic fields perpendicular to the antenna currents using permanent magnets, improved plasma characteristics such as increase of the ion density and decrease of both electron temperature and plasma potential could be achieved in addition to the stability of the plasma possibly due to the reduction of the electron loss. However, the application of the magnetic field decreased the plasma uniformity slightly even though the uniformity within 10% could be maintained in the 800 mm processing area.     

Seat support surface optimization using force feedback

The development, implementation and evaluation of an algorithm designed to find optimal seat support surfaces are presented. The algorithm has been developed and implemented on an active contour measurement device. The device consists of an array of positioning elements equipped with force sensors for feedback. With a patient seated on the array, the algorithm is designed to find a seat contour that optimally satisfies given performance criteria. The performance criteria are based on measured stiffness of the soft tissues. A theoretical development of the algorithm is presented along with the modifications made to the algorithm during implementation. The results from several tests using man-made test bodies and a prototype contour gauge are presented to verify the algorithm's performance     

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.     

孔内凹蚀工艺对芯吸效应的影响分析

高可靠性要求的印制电路板,常要求进行孔内凹蚀以实现孔壁铜对内层铜具有三面包夹的效果增加互联可靠性.本文以实现凹蚀工艺的三个主要步骤:等离子、化学除胶和玻纤蚀刻对芯吸的影响研究为基础,通过SEM、微切片观察各流程后孔壁的微观形貌变化以及PTH后孔铜结构变化,分析了玻纤束及其周边树脂的变化与芯吸长度的关系,确定了各流程对芯...     

大尺寸TFT-LCD ECCP刻蚀工艺低耗整合

为简化大尺寸液晶面板四次光刻法的刻蚀工艺、减少有毒气体使用、降低射频功率消耗,在2 200mm×2 500mm大尺寸玻璃上,采用正交实验设计,验证了功率、气压、反应气体和比例等参数对各刻蚀步骤刻蚀速率、均一性和选择比的影响关系,从而得到各膜层的最佳工艺条件。在Enhance Cathode Couple Plasma Mode(ECCP)刻蚀模式下,采用新刻蚀条件合并薄膜晶体管有源区非晶硅、光刻胶、湿刻后源极和漏极剩余金属钼以及沟道非晶硅层干法刻蚀。利用扫描电子显微镜(SEM)对薄膜电学特性进行测试,结果显示,金属钼的刻蚀可以采用一次两步干法刻蚀,2干2湿刻蚀可以整合为1干1湿。整合后总刻蚀工艺时间减少16s,减少了氯气使用量和RF总功率。试验改进了均一性和刻蚀率,同时对于下底衬具有良好的选择比,保持了良好的形貌,为大批量1干1湿生产提供了依据。     

Development of a magnetron-enhanced plasma process for tungstenetchback with response-surface methodology

A two-step etchback process to form tungsten plugs in submicron contacts and vias has been developed. the process uses an Applied Materials Inc., P5000 WCVD magnetron-enhanced, single-wafer system with an experimental design and response-surface methodology. Tungsten is first etched with an Ar/SF₆ mixture until excited N₂ molecules from the underlying TiN adhesion layer are detected in the plasma. Residual TiN is then etched for a fixed time with an Ar/Cl ₂ plasma. Both steps employ a rotating 0.5-Hz magnetic field. Although the use of the magnetic field has no pronounced effect on the etch rate of either film, it provides broad regions of high etch uniformity. In addition, the DC-bias voltage measured as part of the TiN study decreases with increasing magnetic field strength without reducing the etch rate of the film