Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology

Inverse imaging has been long known to provide a true mathematical solution to the mask design problem. However, it is often times marred by problems like high run-time, mask manufacturability costs, and non-invertible models. In this paper, we propose a mask synthesis flow for advanced lithography nodes, which capitalizes on the inverse mask solution while still overcoming all the above problems. Our technique uses inverse mask technology (IMT) to calculate an inverse mask field containing all the useful information about the AF solution. This field is fed to a polygon placement algorithm to obtain initial AF placements, which are then cooptimized with the main features during an OPC/AF print-fix routine to obtain the final mask solution. The proposed flow enables process window maximization via IMT while guaranteeing fully MRC compliant masks. We present several results demonstrating the superiority of this approach. We also compare our IMT-AFs with the best AF solution obtained using extensive brute-force search (via a first principles simulator, S-litho), and prove that our solution is optimum.

[1]  Bahaa E. A. Saleh,et al.  Image Design: Generation of a Prescribed Image at the Output of a Band-Limited System , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Bahaa E. A. Saleh,et al.  Binary image synthesis using mixed linear integer programming , 1995, IEEE Trans. Image Process..

[3]  Amyn Poonawala,et al.  Model-based assist feature placement: an inverse imaging approach , 2008, Photomask Technology.

[4]  Wei Xiong,et al.  Efficient Mask Design for Inverse Lithography Technology Based on 2D Discrete Cosine Transformation (DCT) , 2007 .

[5]  Yong Li,et al.  Integrating assist feature print fixing with OPC , 2009, Advanced Lithography.

[6]  Peyman Milanfar,et al.  Prewarping techniques in imaging: applications in nanotechnology and biotechnology , 2005, IS&T/SPIE Electronic Imaging.

[7]  C. Vogel Computational Methods for Inverse Problems , 1987 .

[8]  Apo Sezginer,et al.  Model-based assist feature generation , 2007, SPIE Advanced Lithography.

[9]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Linyong Pang,et al.  Inverse lithography technology principles in practice: unintuitive patterns , 2005, SPIE Photomask Technology.

[11]  Yuri Granik,et al.  Solving inverse problems of optical microlithography , 2005, SPIE Advanced Lithography.

[12]  Paul Davids,et al.  Generalized inverse problem for partially coherent projection lithography , 2008, SPIE Advanced Lithography.

[13]  Peyman Milanfar,et al.  OPC and PSM design using inverse lithography: a nonlinear optimization approach , 2006, SPIE Advanced Lithography.

[14]  Peyman Milanfar,et al.  A pixel-based regularization approach to inverse lithography , 2007 .