Mou-Yuan Liao

Photolithography Control in Wafer Fabrication Based on Process Capability Indices With Multiple Characteristics

Photolithography, typically taking about one- third of the total wafer manufacturing costs, is one of the most complex operations and is the most critical process in semiconductor manufacturing. Three most important parameters that determine the final performance of devices are critical dimension (CD), alignment accuracy and photoresist (PR) thickness. Process yield, a common criterion used in the manufacturing industry for measuring process performance, can be applied to examine the photolithography process. In this paper, we solve the photolithography production control problem based on the yield index SPK. The critical values required for the hypothesis testing, using the standard simulation technique, for various commonly used performance requirements, are obtained. Extensive simulation results are provided and analyzed. The investigation is useful to the practitioners for making reliable decisions in either testing process performance or examining quality of an engineering lot in photolithography.

Efficient methods for comparing two process yields – strategies on supplier selection

Selecting an appropriate supplier can substantially reduce purchasing costs, decrease production lead time, increase customer satisfaction, and strengthen corporate competitiveness. Thus, an effective approach to alleviate the problem of supplier selection is essential. Numerous studies have indicated that quality is the most critical and fundamental factor for supplier selection and evaluation, among various criteria. This study provides several methods for selecting the superior supplier based on the commonly used quality criterion, process yield. Four tests for comparing two yield-measure indices, based on the normal approximation and generalised confidence intervals method, are presented and compared. This paper provides recommendations for selecting efficient methods, based on the simulation results of test size and selection power. An example is also presented to illustrate the applicability of these methods.

Economic tolerance design for folded normal data

An important issue for design engineers is how to assign tolerance limits economically. Most work related to tolerance design is for nominal-is-best (N-type) quality characteristics and restricted by a normality assumption. However, smaller-is-better (S-type) quality characteristics and larger-is-better (L-type) quality characteristics are common in real applications. The practical distributions for S-type data or L-type data are typically skewed, and the normality assumption is violated. Determining tolerance with non-normal data using methodologies based on a normality assumption is not appropriate. This study considers the case in which measurements are recorded without their algebraic signs. The folded normal distribution works well to fit these absolute data. Based on the statistical properties of the folded normal distribution, this study develops an economic model encompassing quality loss, manufacturing costs, and re-work costs to determine tolerances. By minimising total costs, a procedure based on the Newton-Raphson method is utilised to obtain the optimal solution. Finally, a welding machine experiment is carried out to demonstrate the applicability of the proposed model.

Process Performance Evaluation with Imprecise Information

Process capability indexes have been widely used in the manufacturing industry for measuring process performance. Most studies on assessing the process capability are based on crisp estimates of process capability indexes while assuming output measurements of the process being precise number. However, in practical situations it is much more realistic that the output characteristics of continuous quantities are more or less imprecise in general. The fuzziness and stochastic uncertainty are unavoidable features of collected observations from continuous quantities. To assess the process performance more accurately, we consider the capability index CPMK, which is recognized as the most useful index to date for assessing the process capability with two-sided specification limits. We construct the fuzzy estimator for the index CPMK according to the fuzzy set theory. To make reliable decisions, we propose a decision rule to assess the process capability based on the fuzzy lower confidence bound of CPMK. A step-by-step procedure for testing process capability is provided for easy execution in practical situations. An example of manufacturing silicon wafers is also presented to illustrate the applicability of the proposed approach.

Capability Testing Based on Subsamples: A Case on Photolithography Process Control in Wafer Fabrication

Photolithography is considered as the bottleneck in semiconductor manufacturing, and a good control of critical dimension, alignment accuracy, and photoresist thickness is essential for maintaining a high quality level of wafers. In this study, a photolithography process in a semiconductor factory is investigated, and the process performance of critical dimension, alignment accuracy, and photoresist thickness measurement is tested based on the process capability index CPMK. Critical values required for hypothesis testing are obtained based on subsamples. This investigation is useful to practitioners in making reliable decision in capability determination. By applying our research results to analyze the process performance with the three critical parameters, a production department can trace and improve the photolithography process.

Fuzzy nonlinear programming approach for evaluating and ranking process yields with imprecise data

Abstract Process yield, the percentage of processed product units passing inspection, is a standard numerical measure of process performance in the manufacturing industry. On the basis of the process yield expression, an index S p k was developed to provide an exact measure of process yield for normally distributed processes. Most traditional studies measuring process capability are based on crisp estimates in which the output process measurements are precise. However, it is common that the measurements of process quality characteristics are insufficiently precise. Traditional approaches for evaluating process yield become unreliable in such cases. Therefore, this study formulates fuzzy numbers to describe the quality characteristic measurements and applies two methods to construct the fuzzy estimation for S p k . A nonlinear programming approach is provided to solve the α-level sets of the estimator, and a testing procedure is presented for making decisions. Finally, this concept is illustrated with an example and extended to solve the ranking problem of multiple yield indices.

Two Tests for Supplier Selection Based on Process Yield

Market share is highly corrected with customer satisfaction and business success; many companies increase their outsourcing level to keep their core competition. Supplier selection problems have become an important issue of production management. Process yield is a standard criterion in the manufacturing industry as a common measure on process performance. The index SPK provides an exact measure on the process yield for normal processes. However, most studies considered the assessment of index SPK for just one single supplier; the testing procedure for suppliers selection has not been done at present. Hence, the principal purpose of this research is to determine the more capable process between two competing suppliers. Two tests are presented based on SPK, and four bootstrap methods are used for obtaining the test statistics. By Monte Carlo simulations, performance comparisons of these methods are performed in terms of empirical size and selection power. Finally, a case on the color filter manufacturing process is investigated to demonstrate the applicability of the proposed method.

Fuzzy inference to supplier evaluation and selection based on quality index: a flexible approach

Supplier evaluation and selection are very important issues in today’s highly competitive global business environment. Many studies have inferred that high quality has a positive, direct impact upon increasing profitability, through lowering operating costs and improving market share; therefore, quality is regarded as the most important factor for supplier evaluation and selection among various criteria. Among various quality control and assurance activities, process capability index Cpk is the most widely used index in making managerial decisions as it provides bounds on the process yield for normally distributed processes. However, existing methods for assessing process performance, which were constructed by statistical inference may unfortunately lead to fine results, because uncertainties exist in most real-world applications. Thus, this study adopts fuzzy inference to deal with testing of Cpk. A brief score is obtained for assessing a supplier’s process instead of a severe evaluation. Moreover, this study extends the proposed approach to conduct the supplier selection problem, which can significantly avoid ambiguous results. Finally, a real example is examined to illustrate the applicability of the proposed approach.

On ranking multiple touch-screen panel suppliers through the CTQ: applied fuzzy techniques for inspection with unavoidable measurement errors

The touch-screen panel (TSP) market has significantly brought up a great deal of business opportunities; in fact, it shows continuous growth in revenue, units, and area. Therefore, the high growth rate of the TSP market forces all the manufactures in the TSP supply chain to face a very competitive environment. The main purpose of this study is to provide a practical procedure for the manufacturers of the TSP terminal application products to rank their TSP suppliers. Via the critical-to-quality (CTQ) tree, CTQ is defined, and the index