Statistics pattern analysis based virtual metrology for plasma etch processes

Virtual metrology (VM) is attracting much interest from semiconductor manufacturers because of its potential advantages for quality control. Plasma etching equipment with state-of-the-art plasma sensors are attractive for implementing VM. However, the plasma sensors requiring physical understanding make it difficult to select input parameters for VM. In addition, those sensors with high sensitivity frequently cause several issues in terms of VM performance. This paper will address plasma sensor issues in implementing a robust VM, where self-excited electron resonance spectroscopy, optical emission spectroscopy, and VI-probe are utilized for critical dimension prediction in a plasma etching process. An optimum sensor selection technique which can give insight into effectiveness of plasma sensors is introduced. In this technique, a numerical criterion, integrated squared response, is proposed for effective selection of important sensors for particular manipulated variables. Sensor data shift across equipment preventive maintenance (PM) and its impact on VM performance are also addressed, where a recursive data centering technique is introduced to handle PM-to-PM sensor data drift in a cost-effective way. The application of the technique introduced in this paper is shown to be effective in dynamic random access memory manufacturing. Hopefully, these results will encourage further implementation of robust virtual metrology in plasma etching for semiconductor manufacturing.

In semiconductor manufacturing, metrology operations are expensive and time-consuming, for this reason only a certain sample of wafers is measured. With the need of highly reliable processes, the semiconductor industry is interested in developing methodologies covering the gap of missing metrology information. Virtual Metrology (VM) turns out to be a promising method; it aims to predict wafer and/or site fine metrology results in real time and free of costs. Using virtual measurements as the input of a sampling decision system (SDS), an optimal strategy for measuring productive wafers can be suggested. Since sampling decisions strongly depend on the accuracy of the VM system, it is a key requirement to monitor the reliability of the obtained predictions. In this paper, we present approaches for dynamically assessing VM reliability using real metrology data. A Bayesian dynamical linear model (BDLM) handles increasing VM model uncertainty over time. Model parameters are updated whenever new real measurements become available. VM prediction quality is monitored applying a probability integral transform (PIT) and scoring rules for predictive probability distributions. Inferring equipment health factors (EHF), unreliable predictions can be detected before being delivered to the SDS. Based on the likelihood of the predicted measurements, VM trust factors are introduced. A Bayesian model for the prediction precision matrix allows updating the virtual measurements' uncertainty whenever real measurements are available. Taking account of the proposed methods, one is led to an improved accuracy of the SDS.

In semiconductor manufacturing, as some of the variables in processes cannot be easily measured during or after the production, virtual metrology (VM) is employed to predict metrology outputs using ancillary process variables. However, because of changeable processes and high-dimensional inputs, VM can be expensive or difficult to implement. In this work, just-in-time (JIT) modeling is used to cope with changes in process characteristics and to automatically update the statistical model. In addition, owing to the non-uniform data distribution, Gaussian process regression (GPR) as a probabilistic approach is a typical method to enhance the robustness of the system in the probability density space. With high-dimensional input variables in deposition processes, a variable shrinkage and selection method for GPR is proposed. It is superior to the conventional methods. The features of the proposed method are shown by way of illustrative examples and the proposed method is compared to conventional work based on real semiconductor process data.

Prediction of plasma etch process by using actinometry-based optical emission spectroscopy data and neural network

A phenomenological-based virtual metrology (VM) technique is developed for predicting the silicon nitride film thickness in multi-layer plasma-enhanced chemical vapor deposition (PECVD). Particularly, the analysis of optical emission spectroscopy based on the excitation kinetics in nitrogen plasma is used to develop novel variables, named plasma-information (PI) variables. One variable, PIWall, is determined by analyzing the light transmittances of the nitrogen emissions at the contaminated window, representing the drift of reactor-wall condition. The other variable, PIVolume, is determined by analyzing vibrational distribution of N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Π <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</sub> , ν = 0-4) states, representing the drift of plasma density and temperature. These PI variables are applied as part of input variables of VM to improve the prediction accuracy. The partial least squares regression is adopted as the statistical method and the contribution of PI variables on the VM are evaluated through the variable influence evaluation on projection. It demonstrates the necessity of PI variables in VM for PECVD and the reactor-wall condition is a major cause of drift in PECVD.

Plasma etch is a complex semiconductor manufacturing process in which material is removed from the surface of a silicon wafer using a gas in plasma form. As the process etch rate cannot be measured easily during or after processing, virtual metrology is employed to predict the etch rate instantly using ancillary process variables. Virtual metrology is the prediction of metrology variables using other easily accessible variables and mathematical models. This paper investigates the use of Gaussian process regression as a virtual metrology modelling technique for plasma etch data.

Samsung SDSThe purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer’s metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads.

Advanced Process Control (APC) is an important research area in Semiconductor Manufacturing (SM) to improve process stability crucial for product quality. In low-volume-high-mixture fabrication plants (fabs), Knowledge Discovery in Databases is extremely challenging due to complex technology mixtures and reduced availability of data for comparable process steps. High Density Plasma Chemical Vapor Deposition (HDP CVD) appears to be a process area in SM predestinated for application of Data Mining (DM). Enhancing physical metrology by predictive models leads to smart future fabs. Actual research focuses on Virtual Metrology (VM) using high sophisticated Machine Learning (ML) methods to model unknown functional interrelations and to predict the thickness of dielectric layers deposited onto a metallization layer of the manufactured wafers. Decision Trees (DT), Neural Networks (NN) and Support Vector Regression (SVR) have been investigated to maximize the accuracy of the regression. For data of various logistical granularities promising results have been achieved by implementing these statistical models.

Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

Decision-based virtual metrology for advanced process control to empower smart production and an empirical study for semiconductor manufacturing

This paper proposes a novel highly accurate virtual metrology (VM) method based on adaptive online time-series learning. The method comprises a novel time-series prediction algorithm robust against data drifts and shifts observed in semi-conductor manufacturing. The prediction algorithm incorporates time-aware data normalization and adaptive online learning. The time-aware normalizer transforms the data to suppress the effect of drifts. The adaptive online learner captures the time-varying relationship between response and inputs caused by shifts. On both simulated and real process data our approach outperforms conventional VM approaches. When applied to advanced process control (APC) on high-volume production lines at a major semiconductor manufacturer, our VM method substantially reduced variability of process outcomes.

The high-value process strategy in the organic light emitting diode display manufacturing requires strict etching profile control to obtain the normal performance of the display pixels because of the correlation between the etched structure of the processed pattern and the current–voltage characteristics of the pixels. The critical dimension, etch depth, and sidewall angle area are important management points of the etching profile. Because of two-dimensional continuous characteristics of the display as the numerous arrays of the thin film transistors, the etching profiles should be controlled in the analog way rather than only the fact of spec-in or -out. Therefore, the sheath properties which govern the ion flux-driven etching effect and the polymer passivation characteristics of the sidewall of the etching hole are needed to be tailored spatially even in the micro-range. The embossing structured electrode surface applied for the large area display manufacturing dry etching process causes micro-scale non-uniformity of the sheath and passivation properties, and introduces the process faults. To control this micro-range non-uniformity of the etching profiles, a PI-VM (Plasma Information based Virtual Metrology) model was developed by applying the PI parameters. PI parameters include the information on the sheath potential for electronegative plasmas and the heat transfer properties in the chamber. PI parameterization utilized a big data pool of the optical emission spectroscopy and equipment engineering system data accumulated during the mass production. The developed PI-VM model could predict the fault with 80% accuracy, and analysis of the highly contributing PI parameters to the events could give the way to control the manufacturing.

This paper presents optical imaging and optical emission spectroscopy (OES) data of an atmospheric-pressure plasma jet. It is shown how the visual information and OES information of the air discharge are related as the blown arc extends from the nozzle (2-4 mm) with a molecular nitrogen rotational temperature on the order of 1700 K and the flowing afterglow beyond this region is dominated by the cooler (300-K) NO-O chemiluminescent reaction that produces NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> species.

Semiconductor manufacturing involves a complex sequence of unit processes, where even a minor error can disrupt the entire production chain. Present-day manufacturing setups rely on continuous data monitoring of equipment health, wafer measurements, and inspections to identify any abnormalities that could impact the quality and performance of the final chip. The primary aim is fault detection and classification (FDC), for which a range of techniques including statistical analysis and machine learning algorithms are commonly employed. In this study, we introduce an innovative approach utilizing an artificial immune system (AIS), inspired by biological mechanisms, for FDC in semiconductor equipment. The main culprits behind process failures are shifts caused by the aging of parts and modules over time. Our methodology integrates state variable identification (SVID) data, reflecting current equipment conditions, and optical emission spectroscopy (OES) data, capturing plasma process information under faulty scenarios induced by deliberate gas flow rate adjustments in semiconductor fabrication. Our results demonstrate a modeling prediction accuracy of 94.69% when incorporating selected SVID and OES data, and 93.68% accuracy using OES data alone. In conclusion, we suggest the potential application of AIS in semiconductor process decision-making, offering promising avenues for enhancing fault detection in semiconductor equipment.