This paper presents the optimization of the grayscale photolithography mask design through a deep-learning workflow, in particular the training dataset optimization thanks to binarization-based mask generation method is studied. To do that, the dithering approach, the new barycentric approach are investigated along with the impact of dataset composition in terms of 3D shapes and mask chromium pattern. The evaluation method to know which dataset provide the best learning ability and at the end 3D shapes close to the targets shapes is presented. Finally, this paper shows that an optimized dataset composed of heterogeneous shapes and mask generated through the proposed barycentric approach giving good results in terms of obtained 3D shape at the end of the workflow in comparison to the homogeneous dataset. Moreover, thanks to these optimized datasets, the obtained 3D shapes at the end of the deep learning workflow is comparable to physics-based mask design method for objects like hemispheres.
In the past decades, the necessity for 3D components became none to be questioned in numerous fields. With these 3D components come new challenges. As an example, the performance of Fabry-Perot based optical filtering devices is dependent on the quality of its sidewalls definition. In this study, we are investigating the mask data preparation method for the patterning of staircase structures thanks to grayscale photolithography. Such structures are used as multiple Fabry-Perot cavities in the context of multispectral imaging. Grayscale photolithography is considered for its offer in 3D micro-fabrication at high throughput. First off, the sidewalls horizontal spread is studied through simulations for different configurations. Metrics are defined and proposed to evaluate the quality of transitions. Secondly, three mask data preparation method are studied : a numerical optimization algorithm as well as an image-to-image deep-learning based workflow are first shown to not meet our application morphological needs. In response, a third method is proposed and is based on local sizings libraries. The recourse to this strategy is shown to have great potential for an improvement of the sidewalls behaviour. However, this methodology taking its first steps, its performance is not pushed to the limit in its current state. As a result, a full mask data preparation flow applied to staircase structures is proposed in the perspectives of this work. With this study we illustrated the importance of the photomask design strategy for the fine tuning of 3D structures sidewalls. We showed the possibility, using a smart mask construction strategy, to produce application-wise enhanced vertical transitions and open the door to further studies toward even steeper sidewalls.
Grayscale PhotoLithography (GPL) enables the patterning of various 3D microstructures in a single lithography step with high throughput. For various 3D optical filtering devices to be functional, high vertical resolution and accuracy are key factors. This precision can, in part, be improved by an adapted mask design construction when using GPL as the 3D patterning method. Here we study different mask design approaches to achieve high resolution staircase like structures patterning using GPL. We found that by using different design flavors, we enlarge the range of available densities for grayscale applications. A relevant design choice also allows us to increase the theoretical vertical resolution enough so that the remaining limitations come not from the mask itself but from the process. The Mask Error Enhancement Factor (MEEF) is also shown to be improvable by tuning the dose sensibility of the design.
KEYWORDS: 3D mask effects, Grayscale lithography, 3D modeling, Data modeling, 3D microstructuring, 3D acquisition, Semiconductors, Profilometers, Process control, Photoresist processing
Optical grayscale lithography offers the possibility to pattern 3D microstructures at large scale and high throughput for HVM semiconductor industry [1-4]. 3D structures uniformity is of importance to ensure homogeneous and at-best performances of several tens of millions of functional elements. This uniformity can be impacted in part by the optical mask variability. Impact of mask variability can be quantified in terms of Mask Error Enhancement Factor (MEEF) [5] for optical grayscale lithography which can be calculated by using resist contrast curve. It has been shown that MEEF is highly dependent on mask densities [5]. Once the mask is fabricated, the impact of mask variabilities on lithography can be controlled by process optimization. In this paper we evaluate the impact of process parameters on optical grayscale MEEF by theoretical and experimental means.
Impact of mask CD errors on microlens and pillar structures fabricated using grayscale lithography technique is studied. CD errors were evaluated from the mask SEM images using contour based metrology. Mask error enhancement factor for grayscale lithography is proposed based on mask (or design) chromium density for given 3D structure to be patterned. Impact of mean-to-target CD mask error and local CD variations on target critical parameters were studied separately. For grayscale lithography, the global mask error enhancement factor calculated to study impact of mask CD errors were found to be non linear and highly dependent on the mask (or layout) chromium density. Surface topography of given grayscale target was found to be highly dependent on the local CD variations. We also found that intentional local CD variation can be used to effectively tune certain target parameters.
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