Jean-Christophe Urbani

Improving model-based OPC performance for the 65-nm node through calibration set optimization

As lithography continues to increase in difficulty with low k1 factors, and ever-tighter process margins, model-based optical proximity correction (OPC) is being used for the majority of patterning layers. As a result, the engineering effort consumed by the development and calibration of OPC models is continuing to increase at an alarming rate. One of the major focal points of this effort is the increasing emphasis on improving the accuracy of the model-based OPC corrections. One of the major contributors to final OPC accuracy is the quality of the resist model. As a result of these trends, the number of sample points used to calibrate OPC models is increasing rapidly from generation to generation. However, this increase is largely due to an antiquated approach to the construction of these calibration sets, focusing on structure variations. In this study, a new approach to the calibration of a resist model will be proposed based upon the location of calibration structures within the actual resist space over which the resist model is expected to be predictive.

High accuracy 65nm OPC verification: full process window model vs. critical failure ORC

It is becoming more and more difficult to ensure robust patterning after OPC due to the continuous reduction of layout dimensions and diminishing process windows associated with each successive lithographic generation. Lithographers must guarantee high imaging fidelity throughout the entire range of normal process variations. The techniques of Mask Rule Checking (MRC) and Optical Rule Checking (ORC) have become mandatory tools for ensuring that OPC delivers robust patterning. However the first method relies on geometrical checks and the second one is based on a model built at best process conditions. Thus those techniques do not have the ability to address all potential printing errors throughout the process window (PW). To address this issue, a technique known as Critical Failure ORC (CFORC) was introduced that uses optical parameters from aerial image simulations. In CFORC, a numerical model is used to correlate these optical parameters with experimental data taken throughout the process window to predict printing errors. This method has proven its efficiency for detecting potential printing issues through the entire process window [1]. However this analytical method is based on optical parameters extracted via an optical model built at single process conditions. It is reasonable to expect that a verification method involving optical models built from several points throughout PW would provide more accurate predictions of printing errors for complex features. To verify this approach, compact optical models similar to those used for standard OPC were built and calibrated with experimental data measured at the PW limits. This model is then applied to various test patterns to predict potential printing errors. In this paper, a comparison between these two approaches is presented for the poly layer at 65 nm node patterning. Examples of specific failure predictions obtained separately with the two techniques are compared with experimental results. The details of implementing these two techniques on full product layouts are also included in this study.

Investigation of model-based physical design restrictions (Invited Paper)

As lithography and other patterning processes become more complex and more non-linear with each generation, the task of physical design rules necessarily increases in complexity also. The goal of the physical design rules is to define the boundary between the physical layout structures which will yield well from those which will not. This is essentially a rule-based pre-silicon guarantee of layout correctness. However the rapid increase in design rule requirement complexity has created logistical problems for both the design and process functions. Therefore, similar to the semiconductor industrys transition from rule-based to model-based optical proximity correction (OPC) due to increased patterning complexity, opportunities for improving physical design restrictions by implementing model-based physical design methods are evident. In this paper we analyze the possible need and applications for model-based physical design restrictions (MBPDR). We first analyze the traditional design rule evolution, development and usage methodologies for semiconductor manufacturers. Next we discuss examples of specific design rule challenges requiring new solution methods in the patterning regime of low K1 lithography and highly complex RET. We then evaluate possible working strategies for MBPDR in the process development and product design flows, including examples of recent model-based pre-silicon verification techniques. Finally we summarize with a proposed flow and key considerations for MBPDR implementation.

Three-dimensional mask effects and source polarization impact on OPC model accuracy and process window

As semiconductor technology moves toward and beyond the 65 nm lithography node, the importance of Optical Proximity Correction (OPC) models grows due to the lithographers need to ensure high fidelity in the mask- to-silicon transfer. This, in turn, causes OPC model complexity to increase as NA increases and minimum feature size on the mask decreases. Subtle effects, that were considered insignificant, can no longer be ignored. Depending on the imaging system, three dimensional mask effects need to be included in OPC modeling. These effects can be used to improve model accuracy and to better predict the final process window. In this paper, the effects of 3D mask topology on process window are studied using several 45 nm node mask structure types. Simulations are conducted with and without a polarized illumination source. The benefits of using an advanced model algorithm, that comprehends 3D mask effects, will be discussed. To quantify the potential impact of this methodology, relative to current best known practices, all results are compared to those obtained from a model using a conventional thin film mask.

Correction of long-range effects applied to the 65-nm node

Specifications for CD control on current technology nodes have become very tight, especially for the gate level. Therefore all systematic errors during the patterning process should be corrected. For a long time, CD variations induced by any change in the local periodicity have been successfully addressed through model or/and rule based corrections. However, if long-range effects (stray light, etch, and mask writing process...) are often monitored, they are seldom taken into account in OPC flows. For the purpose of our study, a test mask has been designed to measure these latter effects separating the contributions of three different process steps (mask writing, exposure and etch). The resulting induced CD errors for several patterns are compared to the allowed error budget. Then, a methodology, usable in standard OPC flows, is proposed to calculate the required correction for any feature in any layout. The accuracy of the method will be demonstrated through experimental results.

65nm OPC and design optimization by using simple electrical transistor simulation

In the context of 65nm logic technology where gate CD control budget requirements are below 5nm, it is mandatory to properly quantify the impact of the 2D effects on the electrical behavior of the transistor [1,2]. This study uses the following sequence to estimate the impact on transistor performance: 1) A lithographic simulation is performed after OPC (Optical Proximity Correction) of active and poly using a calibrated model at best conditions. Some extrapolation of this model can also be used to assess marginalities due to process window (focus, dose, mask errors, and overlay). In our case study, we mainly checked the poly to active misalignment effects. 2) Electrical behavior of the transistor (Ion, Ioff, Vt) is calculated based on a derivative spice model using the simulated image of the gate as an input. In most of the cases Ion analysis, rather than Vt or leakage, gives sufficient information for patterning optimization. We have demonstrated the benefit of this approach with two different examples: -design rule trade-off : we estimated the impact with and without misalignment of critical rules like poly corner to active distance, active corner to poly distance or minimum space between small transistor and big transistor. -Library standard cell debugging: we applied this methodology to the most critical one hundred transistors of our standard cell libraries and calculate Ion behavior with and without misalignment between active and poly. We compared two scanner illumination modes and two OPC versions based on the behavior of the one hundred transistors. We were able to see the benefits of one illumination, and also the improvement in the OPC maturity.

Sensitivity of a variable threshold model toward process and modeling parameters

The quality of model-based OPC correction depends strongly on how the model is calibrated in order to generate a resist image as close to the desired shapes as possible. As the k1 process factor decreases and design complexity increases, the correction accuracy and the model stability become more important. It is also assumed that the stability of one model can be tested when its response to a small variation in one or several parameters is small. In order to quantify this, the small-variation method has been tested on a variable threshold based model initially optimized for the 65nm node using measurements done with a test pattern mask. This method consists of introducing small variations to one input model parameter and analyzing the induced effects on the simulated edge placement error (EPE). In this paper, we study the impact of small changes in the optical and resist parameters (focus settings, inner and outer partial coherent factors, NA, resist thickness) on the model stability. And then, we quantify the sensitivity of the model towards each parameter shift. We also study the effects of modeling parameters (kernel count, model fitness, optical diameter) on the resulting simulated EPE. This kind of study allows us to detect coverage or process window problems. The process and modeling parameters have been modified one by one. The ranges of variations correspond to those observed during a typical experiment. Then the difference in simulated EPE between the reference model and the modified one has been calculated. Simulations show that the loss in model accuracy is essentially caused by changes in focus, outer sigma and NA and lower values of optical diameter and kernel count. Model results agree well with a production layout.

Through-process window resist modelling strategies for the 65 nm node

Ensuring robust patterning after OPC is becoming more and more difficult due to the continuous reduction of layout dimensions and diminishing process windows associated with each successive lithographic generation. Lithographers must guarantee high imaging fidelity throughout the entire range of normal process variations. As a result, post-OPC verification methods have become indispensable tools for avoiding pattern printing issues. The majority of these methods are primarily based on lithographic simulations of pattern printing behaviour across dose and focus variations. The models used for these simulations are compact optical models combined with one single resist model. Even if very predictive resist models exist, they have often a large number of parameters to fit and suffer from long computing times to execute the simulations. Simplified resist models are thus needed to enhance run-time computing during simulation. The objective of this study is to test the predictability of such resist models across the process window. Two different resist models will be considered in this study. The first resist model is a pure variable threshold resist model. The second resist modelling approach is a simplified physical model which uses Gaussian convolutions and a constant threshold to model resist printing behaviour. The study concentrates on poly layer patterning for the 65 nm node. Examples of specific simulations obtained with the two different techniques are compared against experimental results.

Characterization of inverse SRAF for active layer trenches on 45-nm node

Patterning isolated trenches for bright field layers such as the active layer has always been difficult for lithographers. This patterning is even more challenging for advanced technologies such as the 45-nm node where most of the process optimization is done for minimum pitch dense lines. Similar to the use of scattering-bars to assist isolated lines structures, we can use inverse Sub Resolution Assist Features (SRAF) to assist the patterning of isolated trenches structures. Full characterization studies on the C45 Active layer demonstrate the benefits and potential issues of this technique: Screen Inverse SRAF parameters (size, distance to main feature) utilizing optical simulation; Verify simulation predictions and ensure sufficient improvement in Depth of Focus and Exposure latitude with silicon process window analysis; Define Inverse SRAF OPC generation script parameters and validate, with accurate on silicon, measurement characterization of specific test patterns; Maskshop manufacturability through CD measurements and inspection capability. Finally, initial silicon results from a 45nm mask are given with suggestions for additional optimization of inverse SRAF for trenches.

32-nm SOC printing with double patterning, regular design, and 1.2 NA immersion scanner

Resolution Enhancement Techniques (RET) are inherently design dependent technologies. To be successful the RET strategy needs to be adapted to the type of circuit desired. For SOC (system on chip), the three main patterning constraints come from: -Static RAM with very aggressive design rules specially at active, poly and contact -transistor variability control at the chip level -random layouts The development of regular layouts, within the framework of DFM, enables the use of more aggressive RET, pushing the required k1 factor further than allowed with existing RET techniques and the current wavelength and NA limitations. Besides that, it is shown that the primary appeal of regular design usage comes from the significant decrease in transistor variability. In 45nm technology a more than 80% variability reduction for the width and the length of the transistor at best conditions, and more than 50% variability reduction though the process window has been demonstrated. In addition, line-end control in the SRAM bitcell becomes a key challenge for the 32nm node. Taking all these constraints into account, we present the existing best patterning strategy for active and poly level of 32nm : -dipole with polarization and regular layout for active level -dipole with polarization, regular layout and double patterning to cut the line-end for poly level. These choices have been made based on the printing performances of a 0.17&mgr;m2 SRAM bitcell and a 32nm flip-flop with NA 1.2 immersion scanner.