Fall01 coverstory automated method

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An Automated Method for Overlay Sample Plan Optimization Xuemei Chen, Moshe E. Preil, KLA-Tencor Corporation Mathilde Le Goff-Dussable, Mireille Maenhoudt, IMEC, Leuven, Belgium

In this paper, we present an automated method for selecting optimal overlay sampling plans based on a systematic evaluation of the spatial variation components of overlay errors, overlay prediction errors, sampling confidence, and yield loss due to inadequate sampling. Generalized nested ANOVA and clustering analysis are used to quantify the major components of overlay variations in terms of stepper-related systematic variances, systematic variances of residuals, and random variances at the wafer, field, and site levels. Analysis programs have been developed to automatically evaluate various sampling plans with different number of fields and layouts, and identify the optimum plan for effective excursion detection and stepper/scanner control. For each sample plan, the overlay prediction error relative to full wafer sample is calculated, and its sampling confidence is estimated using robust tests. The relative yield loss risk due to inadequate sampling is quantified, and compared with the cost of sampling in determining a cost-optimal sampling plan. The methodology is applied to overlay data of CMP processed wafers. The different spatial variation characteristics of oxide and metal CMP processes are compared and proper sampling strategies are recommended. The robustness of the recommended sample plans was validated over time. The sample plan optimization program successfully detected process change while maintaining accurate and robust stepper/scanner control. Introduction

Shrinking design rules and increasing process complexity have imposed tighter tolerance on overlay control. The number of transistors on a single wafer is increased by more than a factor of four due to increasing wafer size and shrinking feature sizes. In addition, the effects of process non-uniformity coming from deposition and polishing become a significant part in the total overlay budget. As a result, accurate characterization and effective reduction of the variation components of overlay errors, especially spatial variation across a wafer, becomes essential to achieving maximum net good dice per wafer1, and hence yield. Adequate and cost-effective spatial sampling is, therefore, required to detect process excursions and provide confident assessment of the systematic and random components of overlay errors for effective process control. With the increased data points of interest and process complexities, a systematic and automatic sampling optimization approach is necessary. In this paper, we describe an automated method for overlay spatial sampling plan optimization based on spatial variation analysis, overlay prediction error minimization, sample confidence tests, and yield modeling. The optimized sampling plan Fall 2001

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