Kamil Erkan Kabak

An improved metric for measuring multi-item multi-level schedule instability under rolling schedules

Disruptions in material plans due to unrealistic schedules and frequent plan revisions are common symptoms of a phenomenon generally referred to as nervousness or schedule instability in literature. A number of instability measures had been proposed so far. However, none of them deals with instability measurement comprehensively. An appropriate measurement should be able to reflect the degree of changes under rolling schedules, as well as a tool for analyzing the performance of planning systems and nervousness dampening procedures. The intent of this paper is to present a new metric for measuring multi-item multi-level schedule instability under rolling schedules and to compare it with the previous measures in literature. The new metric composed of linear combination of four sub-instability measures separates quantity and timing changes for both scheduled receipts (SRs) and planned orders (PORs). The new metric is tested by a detailed numerical example taken from literature, and results of simulation studies under various experimental factors are presented at the end of the study.

Impact of Recipe Restrictions on Photolithography Toolsets in an ASIC Fabrication Environment

In this paper, a detailed discrete event simulation model is used to better understand the effects of recipe constraints resulting from process restrictions and tool capabilities on overall average cycle time performance of a photolithography area and on average cycle times of individual mask layers. The study is motivated by the industry, in which engineers have to frequently make decisions on tool qualifications and recipe coverage. An experimental procedure is developed and implemented to show the impact of different levels of tool paths on photolithography toolsets. The simulation results show that increasing the number of tool paths decreases the overall average photolithography cycle time for particular wafer loading levels. Also, as start volumes increase, toolset utilizations increase and the impact of single-path tools on average cycle times increases. Immature processes and low-use processes tend to have more single paths and thus suffer higher average cycle times accordingly. Furthermore, it is reported that average cycle time decreases significantly under multiple process environments due to the lower impact of single paths.

Performance improvement for a wet bench tool

Cluster tools are prevalent in wafer fabs. The main reason for this prevalence is that the integration of simple sequential steps together with wafer handling equipment reduces the cost significantly with shared facitilies and smaller foot-prints. This paper analyzes the performance of a wet bench tool in a wet cleaning process by means of a detailed simulation model under different operating factors. The results of the simulation experiments show that through reconfiguration of the recipe sequence types that a 18 % improvement in average hourly throughput can be realised under the same average cycle time.

Analysis of multiple process flows in an ASIC fab with a detailed photolithography area model

ASIC fabs are characterized by multiple process flows. This is mainly due to the highly diversified product portfolios within such fabs. In this study, we first examined the cycle time for individual process flows in a medium volume ASIC fab. We compared these process flows in terms of overall cycle time and using a cycle time index. Secondly, focusing on photolithography we developed a simulation model that employs cycle time data to analyze the impacts of process flow diversity. Thirdly, we used this model to examine the impact on cycle time of changing the volumes of wafer starts on different process flows. The detailed results of simulation experiments along with the concluding remarks are given at the end of the study.

An analysis of tool capabilities in the photolithography area of an ASIC fab

Photolithography is generally regarded as the most constraining element in semiconductor manufacturing. This is primarily attributable to the high capital investment and extensive re-entrant flows throughout this section. Cycle time management in this area is crucial to balance the trade off between tool utilization and cycle time. In a low volume, high product mix fab the inclusion of tool capabilities, and their status, can significantly affect tool utilization and overall cycle times. In this paper a simulation model is developed to aid cycle time decision making policies in the photolithography section of a low volume, high product mix fab. The objective of the study is to determine the optimum course of action, for varying levels of expected increased demand, while maintaining acceptable cycle times and minimizing total capital spent in photolithography. The actions reviewed include the increased use of capabilities where available, followed by the purchase of new photolithography equipment.

Generating operating curves in complex systems using machine learning

This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.

Single toolset modeling approaches in semiconductor manufacturing

Traditional industrial engineering techniques including mathematical models are not sufficient to examine sophisticated manufacturing systems such as semiconductor manufacturing. As such, simulation modeling is used extensively in the design and analysis of semiconductor manufacturing operations. This study explores the use of simulation modeling of single semiconductor toolsets. In the literature a number of modeling approaches for single toolset analysis can be identified. The purpose of this study is to review and evaluate these approaches.

Capacity modelling of a wet bench cluster tool with material handling robot

In this study, a wet bench cluster is examined through a highly detailed simulation model to first understand the tool performance under different process mixes and lot arrival patterns and second to improve the tool performance using different recipe sequences and loading policies. Results of the simulation experiments show that there is benefit in rationalising the number of recipes to increase the opportunity for full loads. In addition, the wet bench capacity is significantly improved by reconfiguration of recipe sequences. The article demonstrates the successful use of a detailed simulation model of a wet bench cluster tool to support process engineering changes.