Introduction to Multivariate SPC

A bunch of Advanced Techniques used for the monitoring and controlling of the operating performance of batch and continuous processes is known as Multivariate SPC (Multivariate Statistical Process Control Chart)

The most important benefit of Multivariate SPC techniques is it reduces the information contained within all the process variables down to two or three composite metrics by implementing statistical modeling technique. These composite metrics can be monitored easily in real-time in order to benchmark the efficiency of the process and determine potential problems, thereby providing a platform for continuous improvements in the operation process.

As the complexity of products and processes increases and the amount of data grows, traditional unilabiate SPC and analytical tools may not be competent enough to provide the insight required by the engineers for their routine activities. Instead, they will need to understand and control processes which are listed by multiple variables, where the relations between the variables are not only complex but also often unknown. The most challenging aspect of this is to make statistical analysis of multiple interdependent variables, more efficient, intuitive, understandable and reliable as unilabiate SPC and analytics.

MULTIVARIATE CONTROL CHARTS:

Multivariate Charts are control charts for variables data. Multivariate Statistical Process Control Charts are used to detect shifts in the relationship (covariance) between several related parameters.

Various different control charts for variables data are available for Multivariate Statistical Process Control analysis:

T2 control charts for variables data, based upon the Hoteling T2 statistic, are used for Analysing shifts in the process. In Place of using the raw Process Variables, the T2 statistic is calculated for The main Components of the process, which are linear combinations of the Process Variables. While the Process Variables may be correlated with one another, the Principal Components are defined in a way so that they are independent, of one another, as required for the analysis.

The Squared Prediction Error (SPE) chart can be used to detect shifts. The SPE is based on the error between the raw data and a fitted PCA (Principal Component Analysis) model (a prediction) to that data.

Contribution Charts are available to as a certain the contributions of the Process Variables to either the Principal Component (Score Contributions) or the SPE (Error Contributions) for a given sample. This is particularly useful for determining the Process Variable that is responsible for process shifts.

Loading Charts give an indication of the relative contribution of each Process Variable towards a given Principal Component for all groups in the analysis.

Few restrictions are an application to these Multivariate Statistical Process Control analyses:

• The process variables are restricted to a subgroup of size one.

• The provision for missing data is not available. If a sample row has an empty cell, this will throw an error message, requiring that either the affected variable/sample should be dropped from the analysis.

• This process specifically excludes PLS (Partial Least Squares) analyses, where the samples for the process variables are linked with quality parameters.

Multivariate SPCMultivariate Statistical Process Control ChartSquared Prediction Error

Computer Integrated Manufacturing (CIM)

IM – Computer Integrated Manufacturing is the approach in manufacturing for using computers to control the entire production process course.

The concept of CIM – Computer Integrated Manufacturing was conceptualized by Dr. Joseph Harrington in his book in the year 1974.

The CIM covers all the processes which are required to convert the customer requirements into output as per customers’ needs. According to U.S. National Research incorporating CIM into our process increases total productivity by 40-70 Percent.CIM decreases design cost by 15-20 Percent. It also reduces lead time by 20 – 60 percent and also cuts down work in progress by 30-60 Percent.

CIM process starts with product designing and ends with product sales.

CIM integration helps processes with information transfer with each other and initiate actions. Benefits of CIM are: Manufacturing can be faster and less error-prone as human intervention is minimal and computers take over the charge, the main advantage is the ability to create automated manufacturing processes. CIM relies mainly on closed-loop control processes, based on real-time input from sensors. This is also known as a flexible design and manufacturing process.

Computer-integrated manufacturing is implemented in the aviation, automotive, space and shipbuilding industries. The concept of “Computer Integrated Manufacturing” is not only a way of manufacturing but also a computer-automated system, where each engineering, production, marketing, and support functions of a manufacturing enterprise are planned efficiently.

In a CIM system, all the functional areas like designing, analysis, planning, purchasing, cost accounting, inventory control, and distribution are connected through the computer with factory floor functions such as materials handling and management, providing direct control and monitoring of all the operations.

As a method of manufacturing, three components distinguish CIM from other manufacturing methodologies:

The Components that distinguish CIM from other Manufacturing methodology’s are:

Means for data storage, Data retrieval, Data manipulation and the way it is presented
Mechanisms for sensing state and modifying processes.
Algorithms used for uniting the data processing component with the sensor/modification component.

CIM is a model of implementation of information and communication technologies (ICTs) in manufacturing. CIM implies that there are at least two computers involved in exchanging information, e.g. the micro-controller and the controller of an arm robot.

Parameters to be considered while incorporating CIM implementation in manufacturing are the production volume, the experience of the company or personnel to make the integration, the level of the integration into the product itself and the integration of the production processes.

CIM is useful where a high level of ICT is used in the facility, such as CAD/CAM systems, the availability of process planning and its data.

Smart Manufacturing in Semiconductor Manufacturing

Semiconductor Manufacturing covers various aspects of manufacturing, which includes wafer manufacturing, chip manufacturing, and product manufacturing.

Wafer Manufacturing includes building electronic circuitry layers on a Wafer.
Chip Manufacturing involves probing and testing.

Product Manufacturing involves the final IC assembling and final testing.

Semiconductor manufacturing is not only challenging but also very complicated production system that involves huge capital investment and advanced technology. Semiconductor product fabrication requires sophisticated control on quality, variability, yield, and reliability.

The most important process in Semiconductor Manufacturing is to automate all the processes. This Automation will make the process sequence and its respective parameter settings more accurate and effective and will also ensure that all the fabs various activities integration are more efficient & reliable.


Automation and integration are the two most important keys to success in modern semiconductor manufacturing.

Let’s talk about challenges in Automation and Integration in the Semiconductor Industry.

Automation has a very important role in the daily operations of semiconductor manufacturing.

Need for Automation in Semiconductor Manufacturing Industry aroused for the reasons which are common to most of the industries which opted for automation. And the reasons leading to automation were to make the process faster, more uniform output, replace humans in processes which could involve working in a hazardous environment

The ultimate goal of automation in semiconductor manufacturing is to eliminate human intervention in fab operations. Fab operations can be broadly classified as Manual, Semi-Automated and Fully Automated.

Manual Mode of operations also knows as a traditional model that does not use any computer assistance in fab tools is very scarce to find in today’s commercial fabs.


Semi-Automated operations still prevail in some 6- and 8-in fabs where processing tools are automated and controlled by computers, but the movement of materials to and from the tools is still handled by fab operators.

Automation in semiconductor manufacturing has to provide the complete state of the art to drive the operations of semiconductor fabrication processes, in which layers of materials are deposited on substrates, doped with impurities, and patterned using photolithography to generate integrated circuits(IC).

Automation in the semiconductor industry adopts the hierarchical machine control architecture that facilitates quick adaptability into current fabrication facilities. In this architecture, the lower level of the hierarchy includes embedded controllers to provide real-time control and analysis of fabrication equipment where sensors are installed for monitoring and characterization. At the higher-level, more complex, context- a dependent combination of processor metrology operations or materials movements are handled, sequenced, and executed.

Cluster tools are used by Contemporary semiconductor manufacturers. Each of these consists of several single-wafer processing chambers, for diverse semiconductor fabrication processes, shorter cycle time, faster process development, and a better yield with less contamination.

Semiconductor manufacturing integration involves- allocation, coordination, and mediation among system dynamics and flows of information, command, control, communication, and materials, in a timely and effective way. Due to the ever-increasing complexity of semiconductor devices and their manufacturing processes, Computer Integrated Systems (CIM) are essential for the smooth integration of semiconductor manufacturing. However, CIM systems generally are loosely coupled, monolithic, and difficult to support the ever-changing needs.

Due to various challenges in Semiconductor Manufacturing Integration, like the emergence of a new application, distributed systems, and data integrity, Researchers and Practitioners are working continuously towards building an integrated framework with common, modular and flexible mode to handle most critical issues in semiconductor manufacturing integration.