In recent years, various methods for hotspot detection during optical proximity correction (OPC) verification have been studied. They try to predict hotspots by analyzing optical features of aerial image such as peak intensity. However, detection accuracy in these conventional methods is still not sufficient. We cannot distinguish hotspots from nonhotspots by only focusing on aerial image of hotspot because one often becomes hotspot and the other does not despite of the same aerial images. On the other hand, optical features of pattern next to the hotspot are different even in such a case. Therefore, optical features which are extracted from surrounding patterns of hotspot are one of the promising metrics for hotspot detection. In this paper, we propose a new method to detect hotspots more accurately. A new metric, Surrounding Optical Feature (SOF), is introduced. SOF indicates optical features which are extracted from surrounding pattern of the evaluated pattern. The optical feature includes critical dimension (CD), normalized image log-slope (NILS), integral intensity, peak intensity of optical image. The proposed method consists of two steps. In step 1, appropriate SOF is extracted by using training data. In step 2, OPC verification is carried out with the SOF. The effectiveness of the proposed method is confirmed in the experimental comparisons.
Self-Aligned Quadruple Patterning (SAQP) will be one of the leading candidates for sub-14nm node and beyond. However, compared with triple patterning, making a feasible standard cell placement has following problems. (1) When coloring conflicts occur between two adjoining cells, they may not be solved easily since SAQP layout has stronger coloring constraints. (2) SAQP layout cannot use stitch to solve coloring conflict. In this paper, we present a framework of SAQP-aware standard cell placement considering the above problems. When standard cell is placed, the proposed method tries to solve coloring conflicts between two cells by exchanging two of three colors. If some conflicts remain between adjoining cells, dummy space will be inserted to keep coloring constraints of SAQP. We show some examples to confirm effectiveness of the proposed framework. To our best knowledge, this is the first framework of SAQP-aware standard cell placement.
Self-Aligned Quadruple Patterning (SAQP) is one of the most leading techniques in 14 nm node and beyond. However, the construction of feasible layout configurations must follow stricter constraints than in LELELE triple patterning process. Some SAQP layout decomposition methods were recently proposed. However, due to strict constraints required for feasible SAQP layout, the decomposition strategy considering an arbitrary layout does not seem realistic. In this paper, we propose a new routing method for feasible SAQP layout requiring no decomposition. Our method performs detailed routing by correct-by-construction approach and offers compliant layout configuration without any pitch conflict.
In this paper, we propose a new flexible routing method for Self-Aligned Double Patterning (SADP). SADP is one of the most promising candidates for patterning sub-20 nm node advanced technology but wafer images must satisfy tighter constraints than litho-etch-litho-etch process. Previous SADP routing methods require strict constraints induced from the relation between mandrel and trim patterns, so design freedom is unexpectedly lost. Also these methods assume to form narrow patterns by trimming process without consideration of resolution limit of optical lithography. The proposed method realizes flexible SADP routing with dynamic coloring requiring no decomposition to extract mandrel patterns and no worries about coloring conflicts. The proposed method uses realizable trimming process only for insulation of patterns. The effectiveness of the proposed method is confirmed in the experimental comparisons.
Computational spacer patterning technology (SPT) has been developed for the first time to address the
challenges concerning hotspots and mask specifications in SPT. A simulation combined with a lithography, etching and
deposition model shows the strong correlation of 0.999, 0.993, 0.980 with the experimental critical dimension (CD),
mask error-enhancement factor (MEEF) and defect printability through a series of spacer processes, respectively.
Furthermore, a design for manufacturability (DfM) flow using computational SPT can find hotspots caused by spacer
patterning processes as well as those caused by lithography process and help designers make the circuit layout more
robust. Besides, a newly defined MEEF and defect printability, which are primary metrics for mask specification, can be
predicted so accurately by using computational SPT that the new scheme to determine appropriate mask specifications is
shown to be feasible under the spacer patterning process condition. Thus, computational SPT is found to be promising
for addressing the challenges concerning hotspot removal and mask specification in the upcoming 20-30nm node and
A neural network (NN)-based approach with a lumped model is found to be much more promising to predict process
proximity effects (PPEs) caused through space patterning processes than a conventional tandem-based approach with a
consecutive physical model. The NN-based lumped approach can improve PPE prediction accuracy by 25% compared to
the conventional tandem-based approach, subject to the same workload of experimental data acquisition, and reach the
specification of PPE residual in 3x nm node with smaller amounts of data volume than any other approach. Process
proximity correction scheme using the NN-based lumped model built for 3x nm node can achieve the expected
correction accuracy for various kinds of one-dimensional patterns. It is anticipated that the NN-based lumped PPE
prediction model will greatly improve the prediction and/or correction accuracy in the space patterning technology
process for 3x nm node and beyond.
Flow of fixing of hot spot induced by optical variation among exposure tools is discussed for quick ramp-up of high volume products. To achieve robust pattern formation for optical variation, following hot spot detection and fixing approaches are introduced: i) at the design stage, hot spot detection within the optical variation space and hot spot fixing by layout modification or OPC optimization, ii) in order to efficiently detect hot spots within the optical variation space, lithography simulation by combinations of optical parameters determined by the design of experiment (DoE), iii) at the manufacturing stage, hot spot fixing by adjustment of optical parameters using the multi-variable optimization to match OPE between the primary and secondary exposure tool.
Robust optical proximity correction (OPC) and design for manufacturability (DFM) methodology for optical
variation among exposure tools is proposed. It is demonstrated that application of the methodology improves standard
deviation of CD difference for target CD by 33% compared with the case of using the conventional methodology. Under
the low-k1 lithography condition, hot spots induced by optical variation among exposure tools delay ramp-up of
production of high-volume products. To realize robust pattern formation for all exposure tools, the following new
methodologies are introduced : i) OPC modeling methodology using actual optics of primary tool, ii) OPC processing
methodology using averaged or designed optics, iii) at the design stage, hot spot detection within the optical variation
space centered on average or designed optics and hot spot fixing by layout modification or OPC optimization, iv) at the
manufacturing stage, hot spot detection using actual optics and hot spot fixing by optical adjustment of troubled tool.