Positioning Repeatability of Robotic Systems With Synchronous Belt Drives

For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

Background: E-beam metrologies, both critical dimension scanning electron microscope metrology and defect scan metrology, have been playing a very critical role in gating patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analyses. However, the low e-beam metrology tool throughput makes it impossible to obtain SEM images for larger area. Monte Carlo-based SEM image simulations or other SEM image simulations require postlithography or postetch pattern three-dimensional structures as prerequisite, and the simulation speed is not sufficiently fast for full chip implementation. Aim: We aim to develop machine learning SEM models with sufficient accuracy and speed for full chip application in semiconductor manufacturing environment. Approach: We have proposed a virtual SEM metrology solution based on U-Net neural network with physics-based feature maps as model input. With information in aerial image space encoded properly, SEM images of both postlithography and postetch can be predicted accurately enough for practical applications using our proposed virtual SEM metrology models. Equipped with GPUs, the machine learning-based SEM image models are fast enough to make it possible to realize full chip SEM generation from post-OPC data. Results: Our machine learning SEM image models can predict SEM images with normalized cross correlation around 0.95 in reference to ground truth SEM images, each SEM image (512 × 512 in size) takes about 800 ms using single CPU, and the speed can be accelerated to about 10 ms with single GPU. Conclusions: Using U-Net structure and physics-based feature maps as model inputs, machine learning-based SEM image models can be developed. The models are sufficiently accurate and fast to find their applications in semiconductor manufacturing, and they can be used as independent model for OPC data verification or generate SEM images as reference for SEM defect scan metrology.

ABSTRACTThe evolution of the excimer laser from a research tool in the 1970's to the industrial models of the 1980's has opened up numerous applications in semiconductor manufacturing, materials processing, and biological and medical research. Present production-capable, computercontrolled excimer systems with handling equipment enabling efficient automation can now operate for up to 50 million shots before undergoing scheduled maintenance, with fluences remaining within ±10% in most cases. Thus, although many uses for the excimer laser are still researchoriented, production applications are rapidly emerging.

The what-if analysis process is essential in symbiotic simulation systems. It is responsible for creating a number of alternative what-if scenarios and evaluating their performance by means of simulation. Most applications use a reactive approach for triggering the what-if analysis process. In this paper we describe a preventive triggering approach which is based on the detection of a future critical condition in the forecast of a physical system. With decreasing probability of a critical condition, using preventive what-if analysis becomes undesirable. We introduce the notion of a G-value and explain how this metric can be used to decide whether or not to use preventive what-if analysis. In addition, we give an example for a possible application in semiconductor manufacturing.

A noncontact temperature measurement technique, diffraction-order analysis, based on monitoring the change in diffraction angle from a grating as a result of thermal expansion, is described and results for both Si and GaAs are presented. Two incident beams are used to provide a differential measurement, independent of wafer tilt. Image processing techniques are used to calculate the relative temperature in near real time from the optical signals. Good agreement between optical and the thermocouple temperature measurements is obtained, with an accuracy and precision of ±0.3 °C demonstrated over a 20–600 °C temperature range for a GaAs sample. Analysis of the effects of all six rigid-body motions of the wafer on the measurement is presented. The measurement is independent of all translational motions; rotational motions (pitch, roll, and yaw) can all be monitored with the same measurement scheme and the temperature measurement corrected for their effects. In many applications in semiconductor manufacturing, wafers are rotated to ensure uniformity. The diffraction-order analysis technique is demonstrated for a rotating wafer with a 3σ precision of 1.95 °C.

Perhydropolysilazane (PHPS) is an important polymeric precursor for producing silica-related inorganic materials through versatile solution-based processing, and has found broad applications in semiconductor manufacturing and microelectronic encapsulation. However, owing to its complex linear and branched architecture, high sensitivity to H2O and O2, and the inherently amorphous nature of the resulting silica or silicon oxynitride, the conversion mechanism of PHPS to SiOx/SiOxNy remains poorly understood. This knowledge gap significantly hampers the rational design and optimization of this promising methodology for the development of high-performance inorganic functional materials. Here, we developed a new Si/N/O/H ReaxFF parameter set capable of accurately describing the thermal conversion of PHPS under various O2/H2O atmospheres. ReaxFF molecular dynamics (MD) simulations reveal distinct atomistic pathways for O2 and H2O: O2 accelerates Si-O bond formation but promotes the growth of a SiOx surface layer that impedes further O2 diffusion, whereas H2O facilitates NH3 release and Si-N bond cleavage. Coordination analysis shows that Si-N tetrahedra are highly sensitive to the atmosphere, with higher O2 levels favoring Si atoms bonded to four N atoms. These findings demonstrate that the Si-O-N composition of PHPS-derived SiOx/SiOxNy can be tuned by controlling the atmosphere during thermal conversion, offering practical guidance for processing optimization and material performance enhancement.

Liquid particle counting (LPC) based upon laser light scattering technology continues to find wide applications in semiconductor manufacturing. Because defect levels in semiconductor devices are related to cleanliness of the processing chemicals, both users and vendors of photoresists and auxiliaries have expended efforts toward LPC testing programs. This paper describes Dynachem's approach to solving problems that have made vendor-user correlations difficult and have prevented setting of meaningful specifications. The paper also discusses how statistical process control can be applied to production of photoresists and developers. As demonstrated in the following discussions, the combination of statistical process control and particulate analysis is powerful and should help vendors and users move toward their goals of specification development.

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.

Commercial quadrupole mass spectrometer (QMS) residual gas analyzers became available in the late 1960s and have been popular in R&D laboratories, but have found limited use in semiconductor manufacturing. To sample at pressures above 10−5 Torr with ppm sensitivity or better (relative to the total process pressure), differential pumping is usually required. The newly available, small, high-pressure QMS sensors can operate as high as 10–20 mTorr without differential pumping, but provide somewhat lower mass resolution and partial pressure sensitivity than a standard QMS. Applications in semiconductor manufacturing include equipment monitoring, process monitoring, and effluent analysis. Equipment monitoring can include qualification after preventative maintenance, rate-of-rise tests, and leak identification and detection. Usually the burning question is, Why won’t the vacuum chamber pump down? Other uses that are not usually considered include the qualification of replaceable parts: sputter cathodes, electrodes, lamps, shields, etc. In process monitoring, the key question is, Is this process running normally? A manufacturing monitor can be useful simply by providing a comparison between a well-behaved high-yield process and a marginal or failing process. Examples are given for physical vapor deposition (sputtering) processes, chemical vapor deposition, and plasma etching. The effluent from chemical vapor deposition and plasma etch processes can be analyzed to measure the efficiency of process gas utilization or to monitor the efficacy of abatement methods used for the removal of global warming gases.

The use of a digital twin as an enabling technology for industry 4.0 provides control systems engineers with novel tools for modelling, designing, and controlling complex systems, providing a deep understanding of the physical asset based not only on its physics but also the real system’s response. It is particularly critical for uniformity temperature control applications, where providing a reasonable model of the system’s diffusion is always affected by the physical behavior of the system’s components required for heating, cooling, or power distribution. In this paper, a digital twin is used to represent a multivariable thermoelectric system employed for temperature uniformity distribution control with potential applications in semiconductor manufacturing. The modelling employs a five-step methodological framework consisting of the stages: target system definition, system description, multiphysics and data-driven simulation, behavioral matching, and implementation to represent the system’s temperature distribution accurately. The temperature distribution is measured using an infrared thermal camera to perform model behavioral matching on heating and cooling temperature uniformity applications. The obtained results indicated that using digital twins not only increases the accuracy of the system’s representation but can also provide the system with novel information that can be leveraged for the design and implementation of smart control systems.

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In the context of modern smart manufacturing, a patent and licensing strategy is formed as a control analytical algorithm for monitoring the technology's readiness for mass production and integrated marketing. New smart manufacturing solutions have a positive impact on the development of most production systems and equipment. Their efficiency is enhanced through vertical and horizontal integration. The proposed technical solutions are based on a method of coding and subsequent identification of the coding element. The method involves applying a special coating (or its technological equivalent) to an object and measuring its thickness. Matching the parameters with the specified code ensures positive identification, while mismatching leads to shutdown or blocking of the equipment or information consumer. This technology has been repeatedly proven in film thickness control applications in solar energy and semiconductor manufacturing. With the advent of multilayer optical discs and recording formats using blue lasers, its importance has increased. Coding at each recording level enables three-dimensional local encryption of information, making the technology particularly relevant for protecting classified and confidential data. In conclusion, it should be noted that new smart manufacturing technologies have a positive impact on the comprehensive development of virtually any production systems and equipment, as well as on their improvement through vertical and horizontal integration.

The selection of orders is the method of gathering the parts needed to assemble the final products from storage sites. Kitting is the name of a ready-to-use package or a parts kit, flexible robotic systems will significantly help the industry to improve the performance of this activity. In reality, despite some other limitations on the complexity of components and component characteristics, the technological advances in recent years in robotics and artificial intelligence allows the treatment of a wide range of items. In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements. The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods. This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations.

An actuation mechanism for a dual-arm scara-type robot for automated pick-place operations in semiconductor and flatpanel-display manufacturing applications is described. The mechanism consists of a pivoting platform and two linkages coupled to the platform through a pair of revolute joints. The opposite ends of the linkages are connected to the upper arms of the robot by another pair of revolute joints, forming a pair of three-bar mechanisms coupled through the pivoting platform. When the platform is in its neutral position, both of the arms are retracted. The geometry of the components of the mechanism is selected so that rotation of the platform from the neutral position in one direction produces desirable radial motion of one arm while the other arm is kept almost stationary in the retracted position. The arms exchange their roles when the platform rotates from the neutral position in the opposite direction. Alternatively, to achieve absolutely no motion of the retracted arm while the other one extends, the pivoting motion of the platform can be replaced by a rocking arrangement with two pivot points. In this configuration, the locations of the two pivot points coincide with the locations of the revolute joints between the linkages and the upper arms when the platform is in the neutral position. As a result, rotation of the platform with respect to one of the pivot points produces no motion of the corresponding arm while the other one extends. Compared to a conventional design, which utilizes a pair of motors coupled to the robot arms through a pair of belt arrangements, the present mechanism eliminates one of the two motors, including its electronic circuitry, and replaces the belt drives with rigid linkages, thus reducing the complexity and cost, and improving the reliability and cleanliness of the robotic system.

The foot and ground force directly affect the propulsion performance and stability of quadruped robot. At present, the typical robots’ foot, such as flat foot, cylindrical foot and spherical foot, are being widely used. However, the bionic foot almost haven’t got very good application. This paper presents a kind of bio-inspired equinus model and a mechanical single leg system. The three dimensional model of equinus had been conducted, and 3D printing technology is also been used to produce bio-inspired foot. To compare the ground reaction force on quadruped robot, this paper conducts tests of hemispherical foot and bio-inspired equinus. Experiments show that the propulsive force of bio-inspired equinus is more excellent than hemispherical foot; and the former makes the robot more stable during walking. The experimental result provides the experimental basis and references for the follow-up study on Bio-inspired foot of quadruped robot.