OPC on a single desktop: a GPU-based OPC and verification tool for fabs and designers

Torunoglu, Ilhami; Karakaş, Ahmet; Elsen, Erich; Andrus, Curtis; Bremen, Brandon; Thoutireddy, P.

doi:10.1117/12.846636

Cited by 7 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, to achieve a truly global optimum at the expense of increased computational cost, all the "unique shapes" can be used to design the source profile. The unique shape finding algorithm presented here is an extension of the work we present in [16]. The fundamental differences are due to the size adjustments for the ambit region to handle the SMO cases.…”

Section: Hierarchical Selection Of Critical Featuresmentioning

confidence: 95%

“…In this step, both the initial source shape, S 0 , and the initial decorated mask, M 0 , are generated. This algorithm is implemented in two stages and in each stage the lithography simulator running on GPU clusters is utilized for contour generation [16]. In the first stage of the initialization, the seed source shape parameters are searched by using a parametric search algorithm.…”

Section: The Initialization Methodsmentioning

confidence: 99%

“…To this extent, one can apply a modification method to all the shapes in the layout to guarantee the best results. Unfortunately, just the modification of the mask shapes for a fixed source shape, S, known as optical proximity correction, has been shown to be very compute intensive [16]. Furthermore, inverse lithography technology (ILT), which is expected to provide better results, is even more compute intense, requiring an even larger number of computational resources than traditional OPC [17].…”

Section: Hierarchical Selection Of Critical Featuresmentioning

confidence: 99%

“…Given the light source, S i , the synthesis of the mask shapes, M i , is done using the method similar to the one described in [16]. In essence, the method simultaneously optimizes mask shapes, SRAF shapes and locations.…”

Section: The Mask Shape Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

A GPU-based full-chip source-mask optimization solution

Torunoglu¹,

Elsen²,

Karakaş³

2010

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

A simultaneous optimization of source and mask with full-chip capability is presented. To provide full-chip processing capability, the solution is intentionally based on GPUs as well as CPUs and made scalable to large clusters while maintaining convergence. The approach uses a proprietary search algorithm to converge to an optimal solution in the sense of print quality maximization while obeying existing mask manufacturing, lithography equipment and process technology constraints. The solution is based on a proprietary optimization function that is applicable to both binary and phase shift masks.

show abstract

Section: Hierarchical Selection Of Critical Featuresmentioning

confidence: 95%

Section: The Initialization Methodsmentioning

confidence: 99%

Section: Hierarchical Selection Of Critical Featuresmentioning

confidence: 99%

Section: The Mask Shape Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

A GPU-based full-chip source-mask optimization solution

Torunoglu¹,

Elsen²,

Karakaş³

2010

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, an efficient yet accurate aerial image simulation becomes a necessity. Various approaches have been proposed to improve its efficiency with different accuracy [1], [2], [11]- [15]. Cong and Zou [2] show that the polygon-based approach can achieve comparable performance while maintaining acceptable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Efficient aerial image simulation on multi-core SIMD CPU

Yan

Wong

2013

2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

View full text Add to dashboard Cite

Aerial image simulation is a fundamental problem in advanced lithography for chip fabrication. Since it requires a huge number of mathematical computations, an efficient yet accurate implementation becomes a necessity. In the literature, GPU or FPGA has demonstrated its potential for accelerating aerial image simulation. However, the comparisons of GPU or FPGA to CPU were not done thoroughly. In particular, careful tunings for the CPU-based method were missing in the previous works, while the recent CPU architectures have significant modifications toward high performance computing capabilities. In this paper, we present and discuss several algorithms for the aerial image simulation on multi-core SIMD CPU. Our fastest method achieves up to 73X speedup over the baseline serial approach and outperforms the state-of-the-art GPU-based approach by up to 2X speedup on a single hex-core SIMD CPU. We show that the performance on the multi-core SIMD CPU is promising, and that careful CPU tunings are necessary in order to exploit its computing capabilities.

show abstract

Accelerating aerial image simulation using improved CPU/GPU collaborative computing

Zhang

et al. 2015

Computers & Electrical Engineering

View full text Add to dashboard Cite

OPC on a single desktop: a GPU-based OPC and verification tool for fabs and designers

Cited by 7 publications

References 0 publications

A GPU-based full-chip source-mask optimization solution

A GPU-based full-chip source-mask optimization solution

Efficient aerial image simulation on multi-core SIMD CPU

Accelerating aerial image simulation using improved CPU/GPU collaborative computing

Contact Info

Product

Resources

About