Optimal slack-driven block shaping algorithm in fixed-outline floorplanning

Abstract

This paper presents an efficient, scalable and optimal slack-driven shaping algorithm for soft blocks in non-slicing floorplan. The proposed algorithm is called SDS. Different from all previous approaches, SDS is specifically formulated for fixed-outline floorplanning. Given a fixed upper bound on the layout width, SDS minimizes the layout height by only shaping the soft blocks in the design. Iteratively, SDS shapes some soft blocks to minimize the layout height, with the guarantee that the layout width would not exceed the given upper bound. Rather than using some simple heuristic as in previous work, the amount of change on each block is determined by systematically distributing the global total amount of available slack to individual block. During the whole shaping process, the layout height is monotonically reducing, and eventually converges to an optimal solution. We also propose two optimality conditions to check the optimality of a shaping solution. To validate the efficiency and effectiveness of SDS, comprehensive experiments are conducted on MCNC and HB benchmarks. Compared with previous work, SDS is able to achieve the best experimental result with significantly faster runtime.

Line Search‐Based Inverse Lithography Technique for Mask Design

“…where I i and z i are the light intensity and the corresponding reaction result of the photoresist at pixel i on the wafer surface, respectively, and t r is the threshold of the photoresist. Thus, the pattern z formed on the wafer surface can be expressed as a function of the mask M based on (2) and (3). In order to make z differentiable so that gradient-based search can be applied, we approximate the above constant threshold model with the sigmoid function…”

“…For modern very large-scale integration (VLSI) design, the traditional VLSI physical design problems (e.g., floorplanning [1][2][3], clustering [4,5], placement [6], and routing) used to play the critical role on coping with the everincreasing design complexity. However, as semiconductor manufacturers move to advanced process nodes (especially 45 nm process and below), lithography has become a greater challenge due to the fundamental constraints of optical physics.…”

An iterative merging placement algorithm for the fixed-outline floorplanning

“…As the aspect ratio (the height divided by the width) of soft module varies continuously, the analytical method [11]- [15] is the most effective algorithm to handle floorplanning with soft modules. Luo et al [11] introduced a nonlinear optimization methodology.…”

“…Given a topological structure, the above two methods could determine a corresponding layout by analytical approaches. Yan et al [15] developed an optimal slack-driven block shaping (SDS) algorithm to shape the soft modules such that the resulting layout is inside the fixed outline.…”

Floorplan-guided placement for large-scale mixed-size designs

“…• An enhanced simulated annealing based framework is adopted as part of the fixed-outline floorplanning step. One of the key enhancement we propose is a slack-driven block shaping algorithm, call SDS [16]. SDS is an efficient, scalable and optimal shaping algorithm that is specifically formulated for fixed-outline floorplanning.…”