Role of CDAFM in achieving accurate OPC modeling

ABSTRACT Accuracy of patterning strongly impacts profit of IC manufacturing and depends on accuracy of optical proximity correction (OPC) and resolution enhan cement technology (RET) models. Despite its importance accuracy of RET and OPC is not known in most cases. Accuracy of CDSEM which is used to build the models is questionable. Sample-to-sample bias variation of CDSEM is exceeding required su b-nanometer uncertainty budget . CDAFM could be used for reference and bias correction. Use of AFM in several areas of RET and OPC modeling is discussed. Accurate inputs and feedback during development reduce number of lear ning cycles and improve quality of the models. Keywords: Accuracy, critical dimension, bias varia tion, measurement uncertainty, TMU, AFM. 1. INTRODUCTION Accuracy of patterning strongly impacts profit of semiconductor integrated circuit (IC) manufacturing through yield, IC performance and time to market. Accuracy of patterning depends heavily on accu racy of optical proximity correction (OPC) and resolution enhancement technology (RET) models. Despite its evident importance accuracy of RET and OPC is not known in most cases. Accuracy of critical dimension (CD) SEM which is used to build OPC and RET models and also to verify final accuracy of patterning is questionable. Currently limited data is available on CDSEM accuracy for one-dimensional (1D) struct ures. Very little is known on CDSEM acc uracy for 2D structures. Recently a new powerful method of OPC development has been introduced [1]. The method uses 2D contours specially designed to tune effectively OPC models. Accuracy of contour CDSEM measurements is simply not known at the moment. Metrology of unprecedented level is re quired to support OPC modeling of 32 nm technology. As suming MOSFET gate CD of 20-30nm expected total process tolerance is then in 2-3 nm range. The gate CD variation has several components: lot-to-lot, wafer-to-wafer, across wafer and intrafield variation including errors of OPC. This leaves for the whole error budget of the 32 nm technology OPC about a na nometer. Further assuming measurement uncertainty-to-process tolerance ratio less than 0.2, one arrives to worry some conclusion that budget of metrology supporting the OPC is only a few Angstroms. Are we up to the challenge? What needs to be done to improve current state of OPC metrology and achieve the required Angstrom range of measurem ent uncertainty? This paper will look into components and origin of measur ement uncertainty (MU) of CDSEM metrology and ways of improving uncertainty and accuracy of metrology for OPC modeling. 2. OPC METROLOGY NEEDS IMPROVEMENT According to the International Technology Roadmap for Se miconductors (ITRS) the measur ement uncertainty contains single tool precision, tool-to-tool matching, sampling uncertainty, and other components. Two other important contributors to MU are sample-to-sample bias variation [2] and probe-sample interaction which for CDSEM includes sample charging, carbonization and shrinkage [3]. Neglecting tool-to-tool matching (since OPC data could be collected on a single tool), all other components of measurem ent uncertainty must be co nsidered and minimized. As a rule precision and sampling uncertainties of OPC metrology can be significantly reduced and brought to the desired range by the proper design of sampling scheme [4] and through use of sufficient number of repeats of the measurement. Reducing component of measurement uncertainty related to measurement bias variation and probe-sample interaction is a much more challenging task. This ta sk requires knowledge of m easurement bias for every OPC sample (process level, process flow, critical dimensions, proximity or layout effects, etc.). To succeed with the task one needs to verify (and correct if needed) bias of CD metrology used to build and check OPC and RET models. Are sample-to-sample bias variation and probe-sample interaction components of CDSEM MU significant? Figure 1 presents bias variation for 1D CDSEM measurements of re sist lines. Two brands of CD SEM are used for comparison. CDAFM is used as a reference tool in this experiment. As it is seen from the figure the CDSEM tools have clearly different bias to reference CDAFM. For SEM_1 bias varies fr om 6 to 10 nm and for SEM_2 bias varies from 2 to 6 nm. In both cases through pitch bias variation is ca. 4 nm what is well above the whole OPC budget. Both tools show similar