A Literature Review on Sampling Techniques in Semiconductor Manufacturing

Abstract

This paper reviews sampling techniques for inspection in semiconductor manufacturing. We discuss the strengths and weaknesses of techniques developed in the last last 20 years for excursion monitoring (when a process or machine falls out of specifications) and control. Sampling techniques are classified into three main groups: static, adaptive, and dynamic. For each group, a classification is performed per year, approach, and industrial deployment. A comparison between the groups indicates a complementarity strongly linked to the semiconductor environment. Benefits and drawbacks of each group are discussed, showing significant improvements from static to dynamic through adaptive sampling techniques. Dynamic sampling seems to be more appropriate for modern semiconductor plants.

Preliminary study of an intelligent sampling decision scheme for the AVM system

“…Lee [5] and Munga et al [6] brought up the concept of dynamic sampling to utilize a more effective inspection and to increase the throughput of inspection tools without affecting the production quality which may achieve the goal of cycle time reduction. Furthermore, a study by Nduhura-Munga et al [7] generalized sampling methods into three categories: static, adaptive, and dynamic samplings. Benefits and drawbacks of each category were discussed in [7], showing significant improvements from static to dynamic through adaptive sampling techniques.…”

“…Furthermore, a study by Nduhura-Munga et al [7] generalized sampling methods into three categories: static, adaptive, and dynamic samplings. Benefits and drawbacks of each category were discussed in [7], showing significant improvements from static to dynamic through adaptive sampling techniques. These sampling methods are to achieve the goal of using real inspections provided to monitor the whole production process and the sampled wafers are the representative for the other related ones.…”

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

“…For example, the metrology systems in semiconductor manufacturing often measure one wafer from each lot of 25 wafers under the assumption that the single wafer is representative of the quality of the whole lot. Such an approach is essentially a static sampling methodology since the sampling rate as well as the sampling location are fixed [2]. The static sampling method is not always effective in practice as wafers are located in different positions and abnormal behaviour of production equipment can occur abruptly, in which case one sampled wafer cannot represent the whole lot [3].…”

“…The static sampling method is not always effective in practice as wafers are located in different positions and abnormal behaviour of production equipment can occur abruptly, in which case one sampled wafer cannot represent the whole lot [3]. Furthermore, static sampling techniques are not suitable for high-mix semiconductor plants because they do not consider factory dynamics and variability [2]. Thus, there is an emerging need in modern semiconductor manufacturing for smarter adaptive/dynamic sampling rules.…”

“…Recent developments in sampling methodologies have focused on dynamic sampling strategies which seek to bring as much information as possible on both risk reduction and process variation [2]. For example, the Dynamic Sampling System (DSS) presented in [7] generates the sampling decision for each lot with the objective of maximizing the probability that the queue waiting for samplings would be subsequently reduced.…”