A Lossless Circuit Layout Image Compression Algorithm for Maskless Lithography Systems

Yang, Jeehong; Savari, Serap A.

doi:10.1109/dcc.2010.17

Cited by 9 publications

(11 citation statements)

References 8 publications

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“…In earlier work, 13 we restricted the contour lines to be either horizontal or vertical and decomposed an arbitrary polygon into a number of Manhattan polygons, i.e., polygons with right angle corners. This decomposition is well-suited to this application because most components of circuit layouts are produced using CAD tools which design the circuit in a rectilinear space, and even the non-Manhattan parts can be easily described by Manhattan components.…”

Section: Corner Transformationmentioning

confidence: 99%

A lossless circuit layout image compression algorithm for electron beam direct write lithography systems

Yang

Savari

2011

Alternative Lithographic Technologies III

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The recent algorithm Corner is a transform-based technique to represent a circuit layout image for electron beam direct write lithography systems. We improve the lossless circuit layout compression algorithm Corner so that 1) it requires fewer symbols during the corner transform, 2) it has a simpler and faster decoding process, and 3) it requires a similar amount of memory for the decoding process.Conventional photolithography systems use physical masks, which are difficult to create and nearly impossible to modify. EBDW (Electron Beam Direct Write) lithography systems are an interesting alternative which bypass physical masks. 1 As illustrated in Figure 1, they instead utilize an electron beam writer to directly write a mask image on a photo-resist coated wafer. There are a number of advantages to EBDW lithography systems. First, the flexibility gained by replacing physical masks with electronic images makes EBDW lithography systems attractive when rapid prototyping is needed for the chip design process.Second, EBDW lithography systems have the potential to be improved by software because the mask images are electronically controlled. This last point will be the focus of this paper.However, EBDW lithography systems have a drawback over physical mask lithography systems: they are slow. 2, 3 A conventional photolithography system can produce the layout images for an entire wafer in one minute. 2 An EBDW lithography system can currently achieve this throughput for circuits with relatively large features, but not for circuits with smaller features because of insufficient bandwidth between the storage where the layer images are deposited and the EBDW lithography system.Dai and Zakhor 2, 4 used lossless image compression to address this problem. As shown in Figure 2, they cache compressed layout images in storage disks and send this compressed data to the processor board memory.Then the EBDW lithography system can be used for layout images with a higher resolution if the decoder embedded within the EBDW lithography writer can recover the original images from the compressed files quickly enough. This type of system has two requirements: 5 1) the compression ratio should be at least (Transfer rate of Decoder to Writer / Transfer rate of Memory to Decoder), and 2) the decoding algorithm must have low enough complexity to be a small addition to the EBDW lithography writer. This second constraint requires the use of a decoder operating with little memory.The layout images of memory cells typically display repeated patterns in contrast to the layout images of control logic circuits. The first lossless image compression algorithm, which is known as C4, 2 is based on context prediction and searching for repeated patterns within an image. Dai and Zakhor later introduced Block C4, 4 which significantly reduces the encoding complexity without sacrificing the compression ratio.

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Section: Corner Transformationmentioning

confidence: 99%

A lossless circuit layout image compression algorithm for electron beam direct write lithography systems

Yang

Savari

2011

Alternative Lithographic Technologies III

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“…Observe that a corner is either the beginning of a line going to the right and/or down or the end of a line. Yang & Savari (2010) (2011) more recently observed that a row (or a column) of the original binary layout image consists of alternating runs of 1s (fill) and runs of 0s (empty). Therefore it is more efficient to encode pixels where there are transitions from 0 to 1 (or 1 to 0) using symbol "1" and to encode the other places using symbol "0."…”

Section: Corner Transformationmentioning

confidence: 99%

“…end for 11: end for Based on the experimental success in Yang & Savari (2010) and Yang & Savari (2011) for binary layout images it is natural to expect that a combination of the corner transformation for the outline of gray-level polygons and a separate representation for the intensity stream would outperform Block C4.Not et h a tnLv-level gray images for this application have pixel intensity 0 (empty) outside the polygon outline, nLv − 1 (fully filled) inside the polygon outline, and an element of (0,nLv) along the polygon outline. Therefore we need only consider intensities along polygon corners and edges.…”

Section: Corner Transformationmentioning

confidence: 99%

“…However, the rasterizing process becomes much less complex if the angle of a contour line is constrained to a small set. Yang & Savari (2010) took advantage of horizontal and vertical contour lines and decomposed an arbitrary polygon into a collection of Manhattan polygons, i.e., polygons with right angle corners. This approach is effective because most components of circuit layouts are produced using CAD tools which design the circuit in a rectilinear space, and the non-Manhattan parts can also be described by Manhattan components.…”

Section: Corner Transformationmentioning

confidence: 99%

“…Liu et al (2007) later proposed Block C4, which significantly reduces the encoding complexity. Based on the framework of Dai & Zakhor (2006) and Liu et al (2007), Yang & Savari (2010) improved the compression algorithm via a corner-based representation of the Manhattan polygons. Their initial algorithm Corner achieves higher compression rates than Block C4 on an irregular circuit.…”

Section: Introductionmentioning

confidence: 99%

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