Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTriple Patterning Lithography (TPL) Layout Decomposition using End-Cutting
106
0
0.0
(
0
)
Added by
Bei Yu
Publication date
2014
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Triple patterning lithography (TPL) is one of the most promising techniques in the 14nm logic node and beyond. However, traditional LELELE type TPL technology suffers from native conflict and overlapping problems. Recently LELEEC process was proposed to overcome the limitations, where the third mask is used to generate the end-cuts. In this paper we propose the first study for LELEEC layout decomposition. Conflict graphs and end-cut graphs are constructed to extract all the geometrical relationships of input layout and end-cut candidates. Based on these graphs, integer linear programming (ILP) is formulated to minimize the conflict number and the stitch number.
rate research
Read More
Triple patterning lithography (TPL) is one of the most promising techniques in the 14nm logic node and beyond. Conventional LELELE type TPL technology suffers from native conflict and overlapping problems. Recently, as an alternative process, triple patterning lithography with end cutting (LELE-EC) was proposed to overcome the limitations of LELELE manufacturing. In LELE-EC process the first two masks are LELE type double patterning, while the third mask is used to generate the end-cuts. Although the layout decomposition problem for LELELE has been well-studied in the literature, only few attempts have been made to address the LELE-EC layout decomposition problem. In this paper we propose the comprehensive study for LELE-EC layout decomposition. Conflict graph and end-cut graph are constructed to extract all the geometrical relationships of both input layout and end-cut candidates. Based on these graphs, integer linear programming (ILP) is formulated to minimize the conflict number and the stitch number. The experimental results demonstrate the effectiveness of the proposed algorithms.
As the feature size of semiconductor process further scales to sub-16nm technology node, triple patterning lithography (TPL) has been regarded one of the most promising lithography candidates. M1 and contact layers, which are usually deployed within standard cells, are most critical and complex parts for modern digital designs. Traditional design flow that ignores TPL in early stages may limit the potential to resolve all the TPL conflicts. In this paper, we propose a coherent framework, including standard cell compliance and detailed placement to enable TPL friendly design. Considering TPL constraints during early design stages, such as standard cell compliance, improves the layout decomposability. With the pre-coloring solutions of standard cells, we present a TPL aware detailed placement, where the layout decomposition and placement can be resolved simultaneously. Our experimental results show that, with negligible impact on critical path delay, our framework can resolve the conflicts much more easily, compared with the traditional physical design flow and followed layout decomposition.
Layout fracturing is a fundamental step in mask data preparation and e-beam lithography (EBL) writing. To increase EBL throughput, recently a new L-shape writing strategy is proposed, which calls for new L-shape fracturing, versus the conventional rectangular fracturing. Meanwhile, during layout fracturing, one must minimize very small/narrow features, also called slivers, due to manufacturability concern. This paper addresses this new research problem of how to perform L-shaped fracturing with sliver minimization. We propose two novel algorithms. The first one, rectangular merging (RM), starts from a set of rectangular fractures and merges them optimally to form L-shape fracturing. The second algorithm, direct L-shape fracturing (DLF), directly and effectively fractures the input layouts into L-shapes with sliver minimization. The experimental results show that our algorithms are very effective.
Recent years have witnessed the fast development of quantum computing. Researchers around the world are eager to run larger and larger quantum algorithms that promise speedups impossible to any classical algorithm. However, the available quantum computers are still volatile and error-prone. Thus, layout synthesis, which transforms quantum programs to meet these hardware limitations, is a crucial step in the realization of quantum computing. In this paper, we present two synthesizers, one optimal and one approximate but nearly optimal. Although a few optimal approaches to this problem have been published, our optimal synthesizer explores a larger solution space, thus is optimal in a stronger sense. In addition, it reduces time and space complexity exponentially compared to some leading optimal approaches. The key to this success is a more efficient spacetime-based variable encoding of the layout synthesis problem as a mathematical programming problem. By slightly changing our formulation, we arrive at an approximate synthesizer that is even more efficient and outperforms some leading heuristic approaches, in terms of additional gate cost, by up to 100%, and also fidelity by up to 10x on a comprehensive set of benchmark programs and architectures. For a specific family of quantum programs named QAOA, which is deemed to be a promising application for near-term quantum computers, we further adjust the approximate synthesizer by taking commutation into consideration, achieving up to 75% reduction in depth and up to 65% reduction in additional cost compared to the tool used in a leading QAOA study.
With continued feature size scaling, even state of the art semiconductor manufacturing processes will often run into layouts with poor printability and yield. Identifying lithography hotspots is important at both physical verification and early physical design stages. While detailed lithography simulations can be very accurate, they may be too computationally expensive for full-chip scale and physical design inner loops. Meanwhile, pattern matching and machine learning based hotspot detection methods can provide acceptable quality and yet fast turn-around-time for full-chip scale physical verification and design. In this paper, we discuss some key issues and recent results on lithography hotspot detection and mitigation in nanometer VLSI.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
