Amr Abdo

The feasibility of using image parameters for test pattern selection during OPC model calibration

Model based optical proximity correction (MB-OPC) is essential for the production of advanced integrated circuits (ICs). Calibration of these OPC resist models uses empirical fitting of measured test pattern data. It seems logical that to produce OPC models, acquiring more data will always improve the OPC model accuracy; on the other hand, reducing metrology and model build time is also a critical and continually escalating requirement with the constant increase in the complexity of the IC development process. A trade off must therefore be made to obtain adequate number of data points that produce accurate OPC models without overloading the metrology tools and resources. In this paper, we are examining the feasibility of using the image parameters (IPs) to select the test patterns. The approach is to base our test pattern selection only on the IPs and verify that the resulting OPC model is accurate. Another approach is to reduce the data gradually in different steps using IP considerations and see how the OPC model performance changes. A third, compromise approach is to specify a test pattern set based on IPs and add to that set few patterns based on different considerations. The three approaches and their results are presented in details in this paper.

Automation of Sample Plan Creation for Process Model Calibration

The process of preparing a sample plan for optical and resist model calibration has always been tedious. Not only because it is required to accurately represent full chip designs with countless combinations of widths, spaces and environments, but also because of the constraints imposed by metrology which may result in limiting the number of structures to be measured. Also, there are other limits on the types of these structures, and this is mainly due to the accuracy variation across different types of geometries. For instance, pitch measurements are normally more accurate than corner rounding. Thus, only certain geometrical shapes are mostly considered to create a sample plan. In addition, the time factor is becoming very crucial as we migrate from a technology node to another due to the increase in the number of development and production nodes, and the process is getting more complicated if process window aware models are to be developed in a reasonable time frame, thus there is a need for reliable methods to choose sample plans which also help reduce cycle time. In this context, an automated flow is proposed for sample plan creation. Once the illumination and film stack are defined, all the errors in the input data are fixed and sites are centered. Then, bad sites are excluded. Afterwards, the clean data are reduced based on geometrical resemblance. Also, an editable database of measurement-reliable and critical structures are provided, and their percentage in the final sample plan as well as the total number of 1D/2D samples can be predefined. It has the advantage of eliminating manual selection or filtering techniques, and it provides powerful tools for customizing the final plan, and the time needed to generate these plans is greatly reduced.

The effect of OPC optical and resist model parameters on the model accuracy, run time, and stability

Performing model based optical proximity correction (MB-OPC) is an essential step in the production of advanced integrated circuits manufactured with optical lithography technology. The accuracy of these models highly depends on the experimental data used in the model development and on the appropriate selection of the model parameters. The optical and resist model parameters selected during model build have a significant impact on the OPC model accuracy, run time, and stability. In order to avoid excessively high run times as well as ensure acceptable results, a compromise must be made between OPC run time and model accuracy. The modeling engineer has to optimize the necessary model parameters in order to find a good trade-off that achieves acceptable accuracy with reasonable run time. In this paper, we investigate the effect of some selected optical and resist model parameters on the OPC model accuracy, run time, and stability.

Model-based calculation of weighting in OPC model calibration

Optimal Proximity Correction (OPC) models are calibrated with Scanning Electron Microscope (SEM) data where the measurement uncertainty vary among pattern types (i.e., line versus space, 1D versus 2D and small versus large). The quality of the SEM measurement uncertaintys impact on OPC model integrity is mitigated through a weighting scheme. Statistical methods such as relating the weight to the SEM measurements standard deviation require more measurements per calibration structure than economically feasible. Similarly, the use of experience and engineering judgment requires many iterations before some reasonable weighting scale is determined. In this paper we present the results of OPC model fitness statistics associated with metrology based weights (MtBW) versus model based weights (MBW). The motivation for the latter approach is the promise for an unbiased, consistent, and efficient estimate of the model parameters.

Meeting critical gate linewidth control needs at the 65 nm node

With the nominal gate length at the 65 nm node being only 35 nm, controlling the critical dimension (CD) in polysilicon to within a few nanometers is essential to achieve a competitive power-to-performance ratio. Gate linewidths must be controlled, not only at the chip level so that the chip performs as the circuit designers and device engineers had intended, but also at the wafer level so that more chips with the optimum power-to-performance ratio are manufactured. Achieving tight across-chip linewidth variation (ACLV) and chip mean variation (CMV) is possible only if the mask-making, lithography, and etching processes are all controlled to very tight specifications. This paper identifies the various ACLV and CMV components, describes their root causes, and discusses a methodology to quantify them. For example, the site-to-site ACLV component is divided into systematic and random sub-components. The systematic component of the variation is attributed in part to pattern density variation across the field, and variation in exposure dose across the slit. The paper demonstrates our teams success in achieving the tight gate CD tolerances required for 65 nm technology. Certain key challenges faced, and methods employed to overcome them are described. For instance, the use of dose-compensation strategies to correct the small but systematic CD variations measured across the wafer, is described. Finally, the impact of immersion lithography on both ACLV and CMV is briefly discussed.

The effect of calibration feature weighting on OPC optical and resist models : investigating the influence on model coefficients and on the overall model fitting

Performing model based optical proximity correction (MB-OPC) is an essential step in the production of advanced integrated circuits that are manufactured with optical lithography technology. The accuracy of these models depends highly on the experimental data used in the model development (model calibration) process. The calibration features are weighted relative to each other depending on many aspects, this weighting plays an important role in the accuracy of the developed models. In this paper, the effect of the feature weighting on OPC models is studied. Different weighting schemes are introduced and the effect on both the optical and resist models (specifically the resist model coefficients) is presented and compared. The effect of the weighting on the overall model fitting was also investigated.

3D physical modeling for patterning process development

In this paper we will demonstrate how a 3D physical patterning model can act as a forensic tool for OPC and ground-rule development. We discuss examples where the 2D modeling shows no issues in printing gate lines but 3D modeling shows severe resist loss in the middle. In absence of corrective measure, there is a high likelihood of line discontinuity post etch. Such early insight into process limitations of prospective ground rules can be invaluable for early technology development. We will also demonstrate how the root cause of broken poly-line after etch could be traced to resist necking in the region of STI step with the help of 3D models. We discuss different cases of metal and contact layouts where 3D modeling gives an early insight in to technology limitations. In addition such a 3D physical model could be used for early resist evaluation and selection for required ground-rule challenges, which can substantially reduce the cycle time for process development.

Three-dimensional physical photoresist model calibration and profile-based pattern verification

In this paper, we report large scale three-dimensional photoresist model calibration and validation results for critical layer models that span 32 nm, 28 nm and 22 nm technology nodes. Although methods for calibrating physical photoresist models have been reported previously, we are unaware of any that leverage data sets typically used for building empirical mask shape correction models. . A method to calibrate and verify physical resist models that uses contour model calibration data sets in conjuction with scanning electron microscope profiles and atomic force microscope profiles is discussed. In addition, we explore ways in which three-dimensional physical resist models can be used to complement and extend pattern hot-spot detection in a mask shape validation flow.

Experiments using automated sample plan selection for OPC modeling

OPC models have become critical in the manufacturing of integrated circuits (ICs) by allowing correction of complex designs, as we approach the physical limits of scaling in IC chip design. The accuracy of these models depends upon the ability of the calibration set to sufficiently cover the design space, and be manageable enough to address metrology constraints. We show that the proposed method provides results of at least similar quality, in some cases superior quality compared to both the traditional method and sample plan sets of higher size. The main advantage of our method over the existing ones is that it generates a calibration set much faster, considering a large initial set and even more importantly, by automatically selecting its minimum optimal size.

Automated sample plan selection for OPC modeling

It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers’ insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.