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Parameterization For Full Shape And Rule Dependent Dissection

IP.com Disclosure Number: IPCOM000009587D
Publication Date: 2002-Sep-04
Document File: 9 page(s) / 81K

Publishing Venue

The IP.com Prior Art Database

Related Documents

09/675,582: PATAPP [+4]

Abstract

According to techniques of the present invention, an optimal bias is computed for a segment in a mask layout using a proximity effects model to account for non-equivalence and non-linearity between changes in mask layouts and changes in printed features. As used here, a bias is a measure of the distance between a segment of an edge in an adjusted fabrication layout, like a mask layout, from a corresponding edge in the original fabrication layout. The edge in the original fabrication layout corresponds to an edge in a design layout. The optimal bias is selected to give an adjusted mask layout that produces an edge in the printed features layer in closer agreement with a corresponding edge in the design layer than is available using conventional procedures.

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Page 1 of 9

Parameterization for Full Shape- Feature Dependent Dissection

Conditions Shape Width

 Space Neighbor

Conditions Shape Width

 Space Neighbor

Conditions

Shape Width

 Space Neighbor

        Parameters Segmentation Length Evaluation Point OPC Spec (tolerance) OPC Parameters (min, max correction)

        Parameters Segmentation Length Evaluation Point OPC Spec (tolerance) OPC Parameters (min, max correction)

Parameters

     Segmentation Length Evaluation Point OPC Spec (tolerance) OPC Parameters (min, max correction)

Polygon EdgesPolygon EdgesPolygon Edges

The purpose is to fully parameterize shape and feature (proximity) based dissection scheme that works with a fully independent dissection engine. This way a fully shape / feature based dissection can be achieved with separate shape matching engine and dissection engine. User will have full control over how to dissect any kind of shape by simply specifying a few parameters. In addition, a lot of information on how OPC should be done can also be attached to each segment, resulting in a rule controlled, model-based OPC.

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Page 2 of 9

Free Dissection of Corners

Dissection points Evaluation points

Previous Edge length

Edge length

angle

Corner dissection 1st anti- serif 2nd anti- serif

Assume we are to dissect a corner freely, i.e., the corner dissection will not interfere or be interfered by dissection of other corners, proximity projections, etc. The corner dissection consists of dissection the corner segment which contains the corner vertex, as well as subsequent anti-serif segments. For each segment there's a segment length associated with it, plus location of evaluation point relative to the dissection, OPC spec and parameters, etc. The conditions on the corner can be the corner angle, the edge length and its neighboring edge lengths (can also stretch to include neighboring corner angles). We can parameterize corner dissection scheme as
[condition] angle, self and neighboring edge length

[dissection] is a matrix, row is length, evaluation point, OPC spec, min/max correction, ..., column is corner,1st AS, 2nd AS, ...]

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Page 3 of 9

Free Dissection of Proximity Projection Points

width

2nd left projection segment

1st right projection segment

                 2nd right projection left st1segment projection
segment

Projection point

Primary distance

Secondary distance

angle

Assume we are to segment around a projection point freely, i.e., the dissection will not interfere or be interfered by dissection of other corners, proximity projections, etc. The projection point dissection consists of dissection into multiple segments on both side of the projection point. For each segment there's a segment length associated with it, plus location of evaluation point relative to the dissection, OPC spec and parameters, etc. The conditions on the segmentation can be distance from the corner, the shape of the corner including the angle and...