Abstract

In semiconductor manufacturing, the time it takes for wafers to process through the line is of utmost importance. Any delay in the processing of these wafers is very costly to the foundry and the end customer. Cycle time is one of the key metrics that any customer looks for in a foundry to ensure that their products are delivered on schedule. To improve overall cycle time, every equipment fleet needs to consistently and efficiently process wafers. In this paper, we will demonstrate sustainable improvements to key manufacturing metrics on Nova OCD fleet.
The key metrics discussed are lot holds, recipe FTR (First-Time Right), fleet availability and fleet matching. Areas of improvement were analyzed, based on which an improvement strategy was developed and executed for each of the metrics. Weekly tracking of the respective metrics showed that the action plan was successful and sustainable. Similar approach could be applied to any metrology fleet to further improve manufacturing metrics.
Keywords: manufacturing efficiency, fleet availability, scatterometry, wafer-less recipe, pattern recognition, fleet matching

Abstract

The majority of scatterometric models used in production control assume constant optical properties of the materials included into the film stack. Only dimensional parameters are assumed as the degrees of freedom. This assumption negatively impacts model precision and accuracy (especially with the trend of scaling down the critical dimensions). In this work we focus on the modeling of Cu and TaN/Ta optical properties in back-end-of-line applications and consider the impact of Cu optical properties modifications in the trenches and as a substrate. We also consider the Cu transparency threshold when Cu acts as a substrate in the film stack. In the case of ultrathin Cu substrate the model output becomes invalid. Quite frequently this fact is not reflected in the goodness of fit. We show that accurate optical modeling of Cu is essential to achieve the required scatterometric model quality for automatic process control in microelectronic production. As a result, we obtain appreciably better matching with electrical data. Therefore, electrical performance can be predicted early in production flow. The modeling methodology presented here can be applied for all technology nodes and also other thin metals such as Co and Ru.
Keywords — Cu, Cu transparency threshold, optical properties, thin metals, optical modeling, scatterometry, OCD

ABSTRACT

With the Area Selective Deposition (ASD) technique, the material is deposited on desired areas of the sample surface. The control of such process implies accurate characterization of the deposited material on both growth and non-growth surfaces. This requires, first a good measurement capability to quantify the geometry of the deposited layer, and second, a proper assessment of the process selectivity. In this work, we show how to combine two complementary measurement techniques to overcome their individual inherent limitations 1 for ASD applications. Scatterometry, the first measurement technique,has been applied to the characterization of the deposited layer geometry properties because of its high sensitivity to dimensional features and material. To complement the ASD performance characterization with the local information,Atomic Force Microscopy (AFM) has been used to access the topography details of the analyzed surfaces. We have analyzed the AFM images with the power spectral density (PSD) approach to identify undesired material deposition in the non-growth area and thus to characterize process selectivity through the comparison to a reference sample. Experimental validation of the scatterometry and AFM techniques for ASD applications has been done on wafers having various selectivity levels. The scatterometry metrology measured accurately the thickness of the deposited layer on both growth and non-growth areas when the deposited layer became uniform. The lateral overgrowth was quantified as well with the same technique and showed some changes from process condition to another. In addition, the PSD analysis applied to the AFM images was able to probe minutely the nanoparticles nucleation on the non-growth area and as result has revealed the selectivity transition regimes. Later, we have built a hybrid model by the combination of the 2 metrologies results and validated its predictions on test wafers.

Keywords: Scatterometry, AFM, Area-Selective Deposition, Power Spectral Density, Hybrid metrology, ALD

Abstract

With growing process complexity and the increasing number of process steps, early prediction of device performance has become an important task in semiconductor manufacturing process control. Machine learning (ML) techniques allow us to link in-line measurements to End-Of-Line (EOL) electrical tests. In our paper, we use reflectance spectra obtained from the scatterometry tool to predict both metal-line resistance and capacitance. We used IMEC N-14 process flow with LELE EUV double patterning at the M1 stage. Special designs-of-experiments (DOE) for multiple parameters allowed us to create a metrology solution for the entire process window and test its accuracy for all POI. Induced variations of both line CDs and space CDs, together with specially designed measurement sites, created wide variations both in metal-line resistance and capacitance. Reflectance spectra were collected in-line at two process steps defining metal lines: HM etch and Cu CMP at multiple targets, including E-test measurement sites, together with reference metrology for overlay (OV) and CD (by using Diffraction-Based-Overlay (DBO) and CD SEM). EOL MT1 electrical test results were used for the ML training procedure for early prediction of patterning effects (both CD and OL) on electrical performance enabling early decisions and cost reduction by discarding out-of-spec wafers before they reached the electrical test. It was shown that ML OCD predictive techniques are complimentary to the OCD model-based solutions for geometrical parameters widely used for in-line APC.

ABSTRACT

Voids in copper lines are a common failure mechanism in the back end of line (BEOL) of integrated circuits manufacturing, affecting chip yield and reliability. As subsequent process nodes continue to shrink metal line dimensions, monitoring and control of these voids gain more and more importance [1]. Currently, there is no quantitative in-line metrology technique that allows voids to be identified and measured. This work aims to develop a new method to do so, by combining scatterometry (also referred to as Optical Critical Dimension or Optical CD) and low-energy x-ray fluorescence (LE-XRF), as well as machine learning techniques. By combining the inputs from these tools in the form of hybrid metrology, as well as with the incorporation of machine learning methods, we create a new metric, referred to as Vxo, to characterize the quantity of void. Additionally, the results are compared with inline electrical test data, as higher amounts of voids were expected to increase the measured resistivity. This was not found to be the case, as the impact of the voids was much less of a factor than variation in the cross-sectional area of the lines.

Keywords: Scatterometry, machine learning, XRF, hybridized metrology, BEOL, voids

ABSTRACT

The epitaxial growth of source/drain structures demands a process with tight control of boron and germanium composition to ensure consistent device performance. However, in-line monitoring of the epitaxial composition in FINFET structures has been one of the most difficult challenges for both process development and manufacturing. Traditional in-line monitoring schemes have relied heavily on critical dimension (CD) measurements, with no composition information. Instead, composition information was provided by offline analysis techniques such as secondary ion mass spectrometry (SIMS), which is destructive and does not measure the composition directly on the FinFET device structure. In this paper, we present results from in-line X-Ray Photoelectron Spectroscopy (XPS) measurements on FinFET structures. This technique is not only sensitive to individual element abundance but also gives information related to the local chemical environment. For this application we monitored silicon, germanium, and boron concentrations in SiGeB EPI source/drain 3D structure without interference from other structural features in the logic device. The in-line XPS measurement of PFET EPI boron and germanium performed in this way on the full structure transistor has been demonstrated to correlate with CMOS device performance, thus significantly reducing time to detect epitaxial composition drift or excursion.

Keywords: In-line XPS, metrology, boron, process monitoring, Epi, germanium, SiGe, FinFETs.