A Fast Mask Manufacturability and Process Variation Aware OPC Algorithm with Exploiting a Novel Intensity Estimation Model

Abstract

With being pushed into sub-16 nm regime, advanced technology nodes printing in optical micro-lithography relies heavily on aggressive Optical Proximity Correction (OPC) in the foreseeable future. Although acceptable pattern fidelity is utilized under process variations, mask design time and mask manufacturability form crucial parameters whose tackling in the OPC recipe is highly demanded by the industry. In this paper, we propose an intensity based OPC algorithm to find a highly manufacturable mask solution for a target pattern with acceptable pattern fidelity under process variations within a short computation time. This is achieved through utilizing a fast intensity estimation model in which intensity is numerically correlated with local mask density and kernel type to estimate the intensity in a short time and with acceptable estimation accuracy. This estimated intensity is used to guide feature shifting, alignment, and concatenation following linearly interpolated variational intensity error model to achieve high mask manufacturability with preserving acceptable pattern fidelity under process variations. Experimental results show the effectiveness of our proposed algorithm on the public benchmarks.

Optical Proximity Correction (OPC) Under Immersion Lithography

“…This algorithm outperforms the most recent effective algorithms on the public benchmarks in terms of pattern fidelity under process variations and runtime. Besides, this algorithm has been extended to improve mask manufacturability in [16] with preserving its effectiveness. This algorithm is analyzed in this chapter and its effectiveness is numerically verified through comparing it with other recent algorithms on the most challenging public benchmarks.…”

“…5. OPC engine framework Figure 9 illustrates the general framework of the OPC engine proposed in [15,16]. Before performing the actual OPC algorithm, a preprocessing phase, wherein, the parameters that guide OPC algorithm are found through regression.…”

“…Thus, a number of parallel lines to each edge in the target are created with d n spacing between each two consecutive lines. In this way, each segment is aligned to the closest line parallel to it after shifting, as shown in Figure 15 [16].…”

“…Let epe prea and epe preb denote the predicted EPE in tap points t a and t b , respectively, after concatenation. Concatenation process is performed as follows [16]:…”

“…In this chapter, recent state-of-the-art OPC algorithms are discussed. Thereafter, a recently published algorithm in [15,16] is deeply analyzed and its performance is verified in terms of pattern fidelity under process variations, mask manufacturability, and computation time.…”