Search

Paper machine reel optimization is defined and explained. A case study is provided to support theoretical considerations. The impact of modern digital control systems on the reel optimization process is discussed.

Paper machine control is a complex control environment. It consists of a large number of smaller control units, typically programmable logic controllers, that are integrated into an overall control architecture. Basis weight and formation control are typical of such systems. Errors in measurements due to sensor malfunctioning, or process states outside the basic assumptions for the control action lead to unwanted and/or poor supervisory response. Manual control often becomes the only way to return the machine to the desired state. In this paper, we report on the initial results of a research to apply the theories of ontological control and the hybrid fuzzy-Boolean finite state machine to paper machine basis weight control. The objective of the research is to provide an automated error detection and recovery method when control encounters an unexpected change in the process environment.

Scheduling and dispatching play a critical role in achieving various key factory goals. Plasma etching or dry etching is one of the many key semiconductor manufacturing process steps. In this paper, we will discuss a dispatching approach that was employed at cluster type plasma etching tools. The loading decisions at cluster type plasma etching are fairly challenging due to highly diverse work in progress (WIP) queue and advanced process control (APC) impact on recipe, chamber qualification. The paper will discuss the loading decision challenges at the tools, possible solution approaches, and the chosen solution with results.

The paper presents a study of the Advanced Process Control (APC) system implemented in refineries. Initially, the paper introduces the Advanced Control System concept and its components. In the second part of the article, a short overview of the Advanced Process Control system is presented. The author’s research was directed to the APC system related to the catalytic cracking plant. Two APC systems implemented in the catalytic cracking plant, one presented in literature and the other found in a Romanian refinery were compared. The common elements and the particularities of each system were identified. Starting from the existing data in the Romanian plant, the authors developed a component of the APC system for the butane-butylene distillation column. The process mathematical models used in the design of the controller in the APC system are presented. The control system with the designed MPC controller is tested for step changes of set-points and disturbances.

Evolution in global markets for chemical and pharmaceutical goods requires new approaches in process plant design. Modularization is commonly considered as a promising approach to face the challenges of changing production environment. In order to exploit the advantages of process modularization, new structures of communication and control are required for modular process automation. Service-oriented architecture has been introduced as a promising communication infrastructure combined with state-based control of services. This paper introduces requirements of modular process automation and general requirements of service-oriented architectures. Process functions are represented by encapsulated services in the control level of the automation pyramid. Interaction between a superior control system and intelligent modules is described. Furthermore, approaches of service-oriented architecture are introduced such as a reference model as well as specific implementations of services within modular automation. Subsequently, four different service-oriented architecture approaches are analyzed with respect to their fulfilment of the presented requirements. Finally, this paper discusses the result of the analysis and the general applicability of service-oriented architectures for modular process automation.

Process automation systems are widely known to be a crucial element in processing plants. Power plants, chemical, petrochemical, oil refining, food, automotive and many other processes in industry are vast and complex, and said to be controlled by just a handful of major players worldwide. Computer control systems and control engineering methods are at the heart of all automatically controlled processes. Motivated by practical success of modern control engineering methods in many industrial process and consumer electronics products there has been an increasing amount of work on development of new methods based on new robust, adaptive methods, numerical optimization techniques, soft computing strategies, microcomputers, decentralized control structure and hardware embedded controllers realization. The paper presents current state and development of the currently most widely used control methods, structures, and their applications to different types of automated processes. The objective of the paper is to survey and compare performance of conventional controllers with several selected advanced controllers with regard to their advantages and drawbacks in industrial applications.

ERP systems have fundamentally changed the way of business data processing. These changes have affected the internal control system of ERP inevitably. Many traditional internal control testing approaches could not work well in ERP environment. In ERP systems, many control criteria and control procedures can be achieved by control data. In this paper, we proposed an applicable approach to test internal controls by checking various types of control data. This approach contains four steps. First is to understand the key business process, second is to identify critical controls points in these processes, the third is to check control data which represented the critical control to test the effectiveness of internal control, and the fourth is to evaluate the history of control data. A practical case is provided at the end of this paper to describe the applicability of this approach.

Industrial plants and processes are becoming very complex and sophisticated. Appropriate control algorithms, being necessary for their efficient operation, can be tested and fine-tuned on simulation models; for advanced process control the models are even necessary. Therefore, the fundamental precondition for control algorithm design is to implement and fine-tune a simulation model. Since plant description methods used nowadays are insufficient for describing modern flexible plants, this paper presents an ontology-based approach to the plant description. The proposed paradigm enables querying and inferring new pieces of knowledge. The solution supports the efficient integration of diverse engineering tools, such as simulators, SCADA systems, or even proprietary software used in particular projects. The proposed approach supports reasoning techniques and efficient utilization of domain specific languages for each engineer or expert. Ontologies comprise explicitly specified knowledge, enable to interchange it between automation tools and can guarantee consistency of the whole integrated solution. The presented approach can be used for both continuous and discrete systems, especially the large-scale ones. A practical example, describing a passive house with ontology individuals and generating a structure of a simulation model automatically by the implemented tool, is involved in this paper.

Traditional semiconductor manufacturing relies on statistical process control and human intervention as the fundamental method to change the process parameters to generate desired output. With the increase in demand for quality, yield, throughput, and performance, Advanced Process Control (APC) is needed to improve the manufacturing processes using run-to-run, wafer-to-wafer, within wafer and real-time process control. In this paper, multivariate, model-based APC system is developed using feed-forward and feedback mechanisms to automatically determine the optimal recipe parameters for each batch based on both incoming wafers and tool state properties. The APC methodology discussed in this paper mostly concentrates on diffusion batch processes. Primarily, it is implemented on the depletion processes such as Low Pressure Chemical Vapor Deposition (LPCVD) nitride. In addition to the traditional LPCVD nitride, APC models have also been tested and deployed on advanced Atomic Layer Deposition (ALD) nitride batch furnace systems. Implementation of APC includes process characterization and understanding of the reaction mechanism of each process. Effects of temperature, time and number of ALD cycles govern the fundamental equations of the model that determine the inputs for subsequent batch. The integration of Equipment Interface (EI), Mathematical Model and the furnace Tokyo Electron Ltd. (TEL) was pivotal for the success of the project. Significant improvement in run to run, lot to lot and wafer to wafer variation in thickness is demonstrated by implementation of APC. In addition to the process parameters, batch size effect is also considered for the APC roll out.

Control engineers usually face in industry to the large amount of complex processes. They need tools for assessing correct performance of control loops. This paper deals with the approach to identification procedures in control performance monitoring for PID control with PLC implementation. There are a lot of tools for performance monitoring on higher level of DCS. These tools are mainly implemented on PC's. In this paper we present implementation of identification procedures and control performance monitoring functions in programmable logic controllers (PLC).

Integration of simulators, data sources, optimizers and other tools is discussed in this paper. More exacting requirements on the quality of the plant operation together with environmental limits and lowering energy intensity bring more complex control systems into the everyday life. When controlling the complex multi-variable systems usually variety of control techniques can be applied in all layers of control systems. The term Advanced Process Control (APC) is used for collaboration of different techniques originate from disciplines like statistics, artificial intelligence, signal processing etc. The most important goals of APC targeted by plant operators are to increase product yield, lower power consumption. The benefits of Advanced Process Control (APC) has been clearly shown in the last years. Mostly used approaches in this area rely heavily upon a study of system behavior and process model and cannot be developed and operated without access to online and historical data. This paper presents an integration platform for simulations and SCADA systems. The main idea is to connect simulations and more complex calculations into the control system architecture. The different control techniques can be utilized by using different tools and algorithms.

Reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives due to it being a high cost, non-value added operation that impacts on cycle-time and throughput. However, metrology cannot be eliminated completely given the important role it plays in process monitoring and advanced process control. To achieve the required manufacturing precision, measurements are typically taken at multiple sites across a wafer. The selection of these sites is usually based on a priori knowledge of wafer failure patterns and spatial variability with additional sites added over time in response to process issues. As a result, it is often the case that in mature processes significant redundancy can exist in wafer measurement plans. This paper proposes a novel methodology based on Forward Selection Component Analysis (FSCA) for analyzing historical metrology data in order to determine the minimum set of wafer sites needed for process monitoring. The paper also introduces a virtual metrology (VM) based approach for reconstructing the complete wafer profile from the optimal sites identified by FSCA. The proposed methodology is tested and validated on a wafer manufacturing metrology dataset.

The paper deals with new directions in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded network control structure and new information and communication technologies. Furthermore, increasing problems with interactions, process non-linearity's, operating constraints, time delays, uncertainties, and significant dead-times consequently lead to the necessity to develop more sophisticated control strategies. Advanced control methods and new distributed embedded control structures represent the most effective tools for realizing high performance of many technological processes. Main ideas covered in this paper are motivated namely by the development of new advanced control engineering methods (predictive, hybrid predictive, optimal, adaptive, robust, fuzzy logic, neural network) and new possibilities of their SW and HW realizations and successful implementation in industry.

In recent years, the high demand for methods to satisfy process plants performance requirements has shed the light on the subject of process control. This subject has become increasingly important as a result of the large emphasis placed on environmental and safety regulations, plant efficiency and economic factors. The primary objective of process control is to maintain a process at the desire operating conditions safely and efficiently, while satisfying environment and product quality requirements [1]. The nonlinear nature of these processes requires other methods than conventional control. Fuzzy controllers are a practical alternative for such processes and provide human experts the ability to control the process by having intuitive understanding of it. Fuzzy controllers are knowledge-based controllers and do not involve or require a mathematical model of the process throughout its design and implementation. This paper addresses the application of fuzzy control to a continually stirred tank reactor (CSTR). The paper demonstrates the feasibility of this method in light of a highly nonlinear chemical process as such.

Software is an important part of industrial process control systems. However, the state-of-the-practice for developing industrial process control software still has several key challenges that need to be addressed (e.g., migration to platforms of different vendors, lack of automation). This paper introduces a model-driven engineering approach to the development of industrial process control software, which is based on the ProcGraph domain-specific modeling language. The paper discusses and offers solutions to several of the development challenges that have not been addressed by existing techniques in the process controls domain. The contributions of the paper are a model-driven engineering approach for the industrial process control domain and a supporting tool infrastructure. The approach is demonstrated by a case study focused on the development of a control system for a TiO 2 (titanium dioxide) pigment production subprocess.

This paper describes an innovative approach to scatterometry modeling, significantly reducing time to solution compared to the industry's current best practices. One of the drawbacks to traditional scatterometry measurement techniques is the time required to optimize the model, which includes material optical constant extraction, model build time, initial model optimization, and model testing. A novel methodology that includes both stability and self-consistent scatterometry accuracy prediction can achieve an order of magnitude gain in productivity over prior methods. This technique creates opportunities among semiconductor chip manufacturers for wider scatterometry adoption at advanced technology nodes, where scatterometry is often the only reliable non-destructive metrology for device structure dimensions. The reductions in cycle time and improvements in accuracy prediction are keys to the success of scatterometry as an enabler for advanced process control and monitoring. This paper presents results on a conventional poly gate litho and final inspection and on leading-edge high-k metal gate after-etch applications. Spectroscopic ellipsometry is used to collect spectra from the gratings on the wafers. Then scatterometry results are obtained using the new approach and via traditional industry-accepted procedures to compare time to solution. To confirm the validity of the results, reference metrology data are collected on a CDSEM and TEM and the total measurement uncertainty is evaluated.

This paper designs and implements a generic, complete Course Teaching-Learning Process Management System, it provides a flexible tracking and controlling of teaching-learning process and an effective management of teaching resources. The system proposes an automatic resource allocation algorithm to improve the efficient use of resources such as classrooms, computer-rooms, etc. The teaching-learning process tracking implements the process evaluation of students based on the data collected from their daily study. Workflow and rule engine modules are designed to support the complex control of teaching-learning process and to achieve the efficient management of teaching activities. With the system proposed in this paper, teaching managers can deal with the course teaching easily and control the course teaching-learning process effectively.

In the era of nanotechnology, variability detection and control in the manufacturing process is vital in delivering products at high yields and at optimal cost. At Intel, DFM techniques are utilized to combat variability at all stages of product delivery-design, tape-out, mask generation, fab processing and assembly. Statistical process control (SPC), advanced process control (APC) and fault detection and classification (FDC) are examples of key capabilities used in the fab for variability detection, containment and correction on processing equipment and manufacturing process. This paper will focus on adaptive metrology sampling (AMS) and its role in variability detection and control during fab processing. AMS includes lot, wafer and field level metrology sampling strategies that varies the amount of sampling based on previous observations which include fab events and/or process control feedback. Adaptive sampling if implemented appropriately will improve precision in excursion detection, reduce detection time, minimize the quantity of silicon at risk and yield losses from such excursions. This paper will illustrate key strategies and techniques that should be considered for a successful AMS implementation. This includes details on system architecture, data inputs, sample material selection rules, and metrology capacity management strategy.

This paper presents a case study for determining the optimal structure of the run-to-run control algorithm for thin film processes. Specifically, the expected process disturbance characteristics are used to determine the nature of the control strategy. Two examples of thin film processes are considered $(i) one involving a simple one-step deposition, and (ii) the other involving two deposition steps, with feedback only possible at the end of both the steps. This paper shows that a unified control structure is capable of handling multiple process types, which traditionally have used different controllers (in terms of their structure), especially in the presence of process shifts and drifts. Furthermore, the paper will also touch upon how the choice of the controller structure depends on the number of context dependent free parameters in the model

Programmable logic controllers (PLCs) are type of process control computers, which are small but relatively inexpensive, environmentally hardened, and easy to program, operate, maintain and repair. The process control computers primarily control devices such as motors, solenoids, pumps, valves, lights and heaters etc. These computers, which number in millions, are the control elements in the virtually all modern factory operations. The programmability of PLC allows for fast and easy changes in relay ladder logic to meet the changing needs of the process or driven equipment without the need for expensive and time consuming rewiring. In this paper a target to develop, simulate and implement PLC software for automation of a water treatment plant is achieved. The algorithm developed in this paper may be used to develop PLC based software for any control process

Wafer warpage is common in microelectronics processing. Warped wafers can affect device performance, reliability and linewidth control in various processing steps. We proposed in this paper an in-situ fault detection technique for wafer warpage in lithography. Early detection will minimize cost and processing time. Based on first principle thermal modeling, we are able to detect warpage fault from available temperature measurements. In this paper, the use of advanced process control resulted in very small temperature disturbance making it suitable for industrial implementation. More importantly, the sensitivity for detecting warpage is not compromised even though the temperature signal is small. Experimental results demonstrate the feasibility of the approach.

APC (Advanced Process Control) has gained increasing attention over the past few years. It is supposed that it is a key for the success in increasing process stability/yield and productivity/costs. The paper gives a definition of the APC disciplines Fault Detection and Classification (FDC), Sensor Systems and Run-to-Run Control (R2R). It highlights the APC history and organization at Infineon, and status of implementation at Infineon's fab Villach, the application of APC in different process areas, examples of achieved benefits, and give an outlook for future requirements. The paper shows specific FDC application examples and benefits from the Infineon Villach fab, e.g. (i) CVD AMAT P5000: prevention of inhomogeneous layers. (ii) Epi AMAT Centura: prevention of slip-lines. (iii) PlasmaEtch: failure investigation on problem lots (tool, process). Automated analysis for critical tool components. Systematic analysis, e.g. for productivity increase. (iv) Litho Canon i5-stepper: prevention of "de-adjusted wafers and reduction of Tool-Alarms". (v) WetEtch: sensors for chemical concentration and defect density.

NCS (networked control system) is a kind of feedback control systems wherein the control loops are closed through real-time control network. The existence of real-time network in the feedback control loop makes the analysis and design of an NCS complex. Conventional control theories must be reevaluated before they can be applied to NCS. Numerous research papers have investigated analysis and design of NCS from different angles. This paper aims to introduce state of arts of NCS and point out the field of further researches.

This paper details an advanced set of analyses designed to drive specific process variable setpoint adjustments or maintenance actions required for cost effective process control using the Dynamic Neural Controller/spl trade/ (DNC) wafer-to-wafer advisories for semiconductor manufacturing advanced process control. The new analytic displays and metrics are illustrated using data obtained on a LAM 4520XL at STMicroelectronics as part of a SEMATECH SPIT beta test evaluation. The DNC represents a comprehensive modeling environment that uses as its input extensive process chamber information and history of the time since maintenance actions occurred. The DNC uses a neural network to predict multiple quality output metrics and a closed-loop risk-based optimization to maximize process quality performance while minimizing overall cost of tool operation and machine downtime. The software responds in an advisory mode on a wafer-to-wafer basis as to the optimal actions to be taken. In this paper, we present three specific instances of patterns arising during wafer processing over time that signal the process or equipment engineer to the need for corrective action: either a process setpoint adjustment or specific maintenance actions. Based on the controller's recommended corrective action set with the overall risk reduction predicted by such actions, a metric of corrective action "urgency" can be created. The tracking of this metric over time yields different pattern types that signify a quantified need for a specific type of corrective action. Three basic urgency patterns are found: 1. a pattern in a given maintenance action over time showing increasing urgency or "risk reduction" capability for the action; 2. a pattern in a process variable specific to a given recipe indicating a chronic request over time to only adjust the variable setpoint either above or below the current target; 3. a pattern in a process variable existing over all recipes processed through the chamber indicating chronic request to adjust the variable setpoint in either or both directions over time. This pattern is a pointer to the need for a maintenance action that is either corroborated by the urgency graph for that maintenance action, or if no such action has been previously taken, a guide to the source of the equipment malfunction.

A multi-component Advanced Process Control (APC) system has been developed and deployed for an Applied Materials vapor phase epitaxy tool at Fairchild Semiconductor. The system, which is fully automated, operates on a SEMI APC framework compliant platform, supports the plug-and-play of multiple APC applications ("plug-ins"), and has configurable control rules that allow the user to specify not only how each APC application will be utilized, but also how the APC applications will interact in a complementary fashion. The current system contains real-time data visualization, run-to-run control (R2R), and fault detection and classification (FDC) plug-ins. The data visualization component is currently being utilized for real-time visual monitoring of aspects of equipment health. The R2R component includes a multivariate algorithm and is being used to provide multi-zone control of deposition thickness and resistivity. The FDC component will gather the in-process data from the tool to automatically determine equipment health and classify faults in order to reduce mean time to repair. In this paper, the methodology used to deploy and configure this APC system is described. Data is provided illustrating the use of each of these APC capabilities individually and in complementary fashion. Data analysis is included that focuses on the benefits of utilizing these capabilities, benefits such as improved equipment reliability, improved overall equipment effectiveness and yield. The paper also includes a discussion of the benefits and issues associated with the complementary utilization of multiple APC components in an open architecture framework.

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

This paper presents an abridged history of Advanced Process Control (APC), including both Fault Detection and Classification (FDC) and Model Based Process Control (MBPC), both within TI and in the semiconductor industry. While TI was an early leader in univariate fault detection in processing tools, other manufacturers have by now implemented such methodologies. For MBPC, the MMST program gave TI a lead, but others are now following that path. For TI and the semiconductor industry as a whole, the current thrust is to develop and implement multivariate APC methods into the manufacturing operations. This paper describes the complexity of the execution of these tasks, and lists some of the available tools that are requisite for implementing these plans.

This paper discusses the practical application of continuous performance assessment as a requisite for maintaining the advanced process control (APC) assets in the process industry. The commissioning of elaborate control system platforms, advanced control applications, and information management systems e.g. historians and databases etc., have become commonplace in the process industry. Incidentally, these investments have led to the accumulation of tremendous process data with few data-mining tools and control-relevant techniques for extracting information. In this paper, the theory of performance assessment is presented as a technique for continuously assessing the performance of the basic control loops in a plant, as a means towards maintaining APC assets. Continuous performance assessment allows detection of performance degradation in the basic control loops in a plant by monitoring the variance in the process variable and comparing it to that of a minimum variance controller. The resulting index gives an indication of the level of performance of the controller and an indication of the action required to improve its performance, e.g., re-tune the controller or consider process re-engineering. A process application of this technique will be demonstrated with the use of a software tool-ProcessDoc/sup TM/-which computes loop performance indices from archived routine operating data from an industrial process.

What process control calls advanced control has always been hard to understand and address operationally. For its users, like other forms of software, "It sucks!" (S. Alsop, 1996). The more recent commercial acceptance of mathematical advanced controls may in fact be more due to their clearer, more predictable, and therefore more contractible nature, than to their improved control. Professionals seriously involved in trying to re-establish traditional advanced controls in an economic environment where you have to run "to stay in the same place", should begin to understand the lessons of software applied to control. The key notion is information modeling, which relates to our familiar math modeling, but formalizes application organization for human analysis rather than mathematical analysis. Objects are the current information modeling buzz word, but patterns, building on objects, are a newer advance; both will be important to maturing the software profession, suggesting directions for control application discipline. The paper discusses the information modeling needed to integrate and discipline our control software. Previous papers have discussed a language, reflecting these ideas and targeting a minimal 3:1 improved readability and comprehensibility, demonstrable and translating to a 3:1 reduction in total cost of the application engineering. The paper briefly illustrates the discussion with a few of the language features.

The purpose of the virtual manufacturing (VM) concept is to integrate existing manufacturing models, techniques of analysis and forms of representation in a consistent system in such a way that concurrent engineering within the planning of a new product or manufacturing system can be accomplished. In this paper, the VM concept in process control is put forward. Then, on the basis of the concept of process control, the DCS and the VM concept, an environment of an industrial control computer system including model creation, control, optimization and simulation is set up. For the example and testing of the VME for process control, the simulation system for a pulp and paper mill is established. The result of the simulation is satisfactory.

The paper describes a controller performance assessment (CPA) method. The method is applied to evaluate advanced process control strategies using an interacting tank system (ITS). Since this plant has been specially designed with characteristics and problems inherent to the actual process industry environment, it is a test-bed with the necessary creditability for one to draw conclusions about controller performances. The CPA method aims at accomplishing the performance assessment of a control system, running on line, in real time. It is based on measurements of the control signal, the output signal and the error signal. The performance evaluation is centered on the system users' views. The users considered are: the system manager, the system operator and the control engineer. The method allows the development of comparative studies of the controllers' performances. The paper presents such studies for the following SISO controllers in a multi-loop strategy: generalized minimum variance (GMV), generalized predictive controller (GPC) and a fixed parameter PID. The performances' comparative studies for a MIMO-GMV and a multivariable fuzzy controllers are also presented.

Today's engineers are faced with an ever-increasing number of choices in the design of industrial control systems. The selection of control voltage, while often perceived as a simple decision, has potentially the largest impact on the design and overall success of a system. This paper focuses on the comparison of application issues for two frequently used control schemes: 24 V DC and 120 V AC. Topics such as safety, device functionality, system design and wiring methods, reliability, and cost are explored to identify important considerations in the selection process.

In this paper an emerging trend towards distribution in the diverse disciplines of control systems engineering and innovation theory is identified. Developments in Distributed Control Systems (DCS) and Innovation Theory are discussed and the correlations between trends in the two disciplines are highlighted. The practical implications of distribution are then considered through a case study analysis of a research and development project that is developing an integrated environment for specification and implementation of open control systems. The paper highlights technological and innovation management lessons which may be of use to practitioners.

The die chipping which cause function failure always a key issue for BiCSX wafer saw. Traditionally, detecting the defects by die seal ring inspection after it has occurred has been large-scale adoption. However, this method has large limitation due to two factors. First is nuisance defect which may cause by manual classification; second is additional investment from detection machine. Fault Detection and Classification (FDC) as one part of Advanced Process Control (APC) has been widely studied in semiconductor manufacturing. FDC can detect the defects like die chipping by record and analysis equipment data during wafer saw. It can not only detect the defects but also help engineer to develop robust process. In this paper, the mechanism of die chipping and the interaction of equipment data with defects were studied. Successfully create a totally new FDC rule to detect the die chipping.

The verification of a new or changed recipe is a common process control task in a semiconductor factory. It is also a prime example of the demanding integration challenges that Advanced Process Control (APC) is facing today. State-of-the-art process control solutions must be fully integrated with the work-in-progress (WIP) fabrication control solutions to enable full automation and to find the optimal tradeoff between competing Fab Key Performance Indicators (KPIs). The recipe verification presented in this paper illustrates a holistic strategy that brings these areas together.

In the mining industry, thickeners are used to increase density of slurries by removing water. When a thickener is in operation, the underflow slurry density should follow a given setpoint and stay inside a boundary layer. In general, thickeners are manually controlled. However, industrial experience has shown that manual control is not sufficient. For this reason, an advanced process control strategy is proposed in this paper. On the one hand, using historical data, a linear dynamic model of the studied thickener is developed. In this regard, data is collected, prepared, and data informativity is studied to ensure model identifiability and interpretability. On the other hand, a model predictive control is designed to control the process by manipulating the feed and the underflow slurry flowrates. The simulation results show that the method can be successfully used to control the underflow slurry density in the presence of feed variations and unmeasured disturbances.

The Case-Based Reasoning (CBR) as a tight integration of the model-based and data-driven approaches in optimization and control of plants with distributed parameters (DPP) is considered into this second part of the paper. The main pecularities of the adopted modifications of CBR in accordance of the area of application are examined – structuring of search space as virtual version space, retrieval for different form of attribute-value presentations, efficient indexing and adaptation using local regression in order to improve retrieved solution into the direction to obtain query problem solving. The main results of the accomplished simulations are reported focusing both the maximum capacity and minimum heat consumption. The results show that suboptimal control of TTP based on modified CBR is a reasonable approach.