On-product overlay (OPO) is a critical inline process control parameter in semiconductor manufacturing. One of the main factors that induce the overlay error is non-lithography processes like etching, deposition and cleaning. The overlay margin is getting tighter as the device technology advances and detecting the root cause of process-induced overlay error is a main problem to improving the OPO. However, it is not an easy problem to solve due to the lack of inline monitoring data on non-lithography processes. Even if we evaluate inline monitoring data, it is too sparse to do in-depth analysis compared to abundant lithographic overlay data. Instead, we can make use of data from the PWG patterned wafer geometry metrology system, which can measure high-density data with high throughput. In this paper, we introduce a comprehensive method of detecting the root cause of the process-induced overlay errors based on inline PWG data. Our target device is a 3D NAND product with process-induced overlay errors due to wafer geometry. We start our analysis by tracing PWG GEN3 data for the same wafer in a wide process step range. We compare the GEN3 signature to an overlay error signature of a target lithography layer to filter out suspicious processes. From the suspicious processes, we derived optimized KPIs that discriminate between good and bad wafers in terms of process-induced overlay errors, which are then used as a monitoring metric. With the optimized KPIs, we discern which process is the root cause of process-induced overlay errors to help drive corrective actions and improve OPO on the target device. Finally, we propose a comprehensive framework that is not limited to PWG data but applies to other available inline data such as alignment, ADI and AEI overlay and NZO.
Critical dimension uniformity (CDU) control using dose correction is well established and has relied on traditional polynomial models like Zernike and Legendre for a long time. As process margins are shrinking and CD (and CDU) control becomes a significant contributor to edge placement error (EPE), the dose correction models need to be enhanced to represent the systematic behavior of the fingerprints more precisely. In this paper we show that many CD signatures over the exposure field or over the wafer cannot be corrected efficiently using classical polynomials. As the CD signatures can come from a variety of processes like etch, CVD, polish, or deposition, a flexible model approach is required. Furthermore, making the right decision when choosing the correct model order of the classical polynomial based model is complicated as we need to handle the balance between the degrees of freedom and minimizing the residuals. With this problem statement in mind, we introduce a novel radial basis function (RBF) modeling approach for dose corrections that can correct a wide range of signatures. The new modeling approach is verified on real CD signatures on product, reducing CDU significantly. Additionally, we demonstrate that this approach can make the life of the engineers easy again, as there are no prior decisions about model type and order needed.
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