Demand-driven dynamic control points insertion for High-fidelity and efficient curvilinear optical proximity correction.

Curvilinear optical proximity correction is critical for advanced technology nodes. However, conventional methods rely on uniform or static control points distribution. Such strategy neglects correction demands of different region and the influence of adjacent regions, leading to inefficient resource allocation and sub-optimal imaging fidelity. A demand-driven dynamic control point insertion framework is introduced to overcome the control points distribution inefficiency in curvilinear optical proximity correction. Our method begins with a sparse initial set of control points and iteratively identifies critical regions. By leveraging the local support property of B-spline curves, new control point is strategically inserted into critical region, while adjacent points are adjusted to shift the critical region. By bypassing redundant uniform initialization, this method achieves high-fidelity and efficient curvilinear optical proximity correction while resolving inherent resource misallocation. This paper presents a novel dynamic control points insertion framework designed to address the fundamental flaws in initial control point distribution for curvilinear mask. Simulations demonstrate proposed method’s capability to establish rational control point distributions, thereby achieving high-fidelity and efficient optical proximity correction.