Classical SEM metrology, CD-SEM, uses low data rate and extensive frame-averaging technique to achieve high-quality SEM imaging for high-precision metrology. The drawbacks include prolonged data collection time and larger photoresist shrinkage due to excess electron dosage. This paper will introduce a novel e-beam metrology system based on a high data rate, large probe current, and ultra-low noise electron optics design. At the same level of metrology precision, this high speed e-beam metrology system could significantly shorten data collection time and reduce electron dosage. In this work, the data collection speed is higher than 7,000 images per hr. Moreover, a novel large field of view (LFOV) capability at high resolution was enabled by an advanced electron deflection system design. The area coverage by LFOV is >100x larger than classical SEM. Superior metrology precision throughout the whole image has been achieved, and high quality metrology data could be extracted from full field. This new capability on metrology will further improve metrology data collection speed to support the need for large volume of metrology data from OPC model calibration of next generation technology. The shrinking EPE (Edge Placement Error) budget places more stringent requirement on OPC model accuracy, which is increasingly limited by metrology errors. In the current practice of metrology data collection and data processing to model calibration flow, CD-SEM throughput becomes a bottleneck that limits the amount of metrology measurements available for OPC model calibration, impacting pattern coverage and model accuracy especially for 2D pattern prediction. To address the trade-off in metrology sampling and model accuracy constrained by the cycle time requirement, this paper employs the high speed e-beam metrology system and a new computational software solution to take full advantage of the large volume data and significantly reduce both systematic and random metrology errors. The new computational software enables users to generate large quantity of highly accurate EP (Edge Placement) gauges and significantly improve design pattern coverage with up to 5X gain in model prediction accuracy on complex 2D patterns. Overall, this work showed >2x improvement in OPC model accuracy at a faster model turn-around time.
Strong resist shrinkage effects have been widely observed in resist profiles after negative tone development (NTD) and therefore must be taken into account in computational lithography applications. However, existing lithography simulation tools, especially those designed for full-chip applications, lack resist shrinkage modeling capabilities because they are not needed until only recently when NTD processes begin to replace the conventional positive tone development (PTD) processes where resist shrinkage effects are negligible. In this work we describe the development of a physical resist shrinkage (PRS) model for full-chip lithography simulations and present its accuracy evaluation against experimental data.
Computational lithography has become indispensable when developing lithography solutions for advanced technology nodes. One of the essential instruments for optimizing full-chip process windows (PW) is source mask optimization (SMO). To avoid model calibration for each new optimized source, separable resist models need to be created such that a reliable model can be obtained simply by replacing the source in the existing OPC model. In this paper we start from a fully calibrated resist model and optimize a new source for which we want to create a reliable OPC model. Relying on the separability of the model, the initial illumination source is replaced by the new one while not changing any resist model parameters. In order to reach the accuracy needed for OPC, the best focus and best dose still need to be accurately determined. We will investigate two models that have the same new SMO source and original resist model. For one model the best focus and dose are determined by the simulated Bossung plot of one anchor feature. The second model’s best focus and exposure are determined by a small set of FEM experimental data. The quality of these two models is then evaluated by comparing them to a reference model, which is fully calibrated using a complete dataset for the new source. We show that the calibrated FEM OPC model can be extrapolated by simply changing the source. A limited amount of experimental FEM data is required to accurately determine the best focus and exposure for the new source. Best focus and exposure based on the anchor pattern simulation has a higher degree of uncertainty compared to a small set of experimental data.