No abstract

Scatterometry is an important nonimaging and noncontact method for optical metrology. In scatterometry certain parameters of interest are determined by solving an inverse problem. This is done by minimizing a cost functional that quantifies the discrepancy among measured data and model evaluation. Solving the inverse problem is mathematically challenging owing to the instability of the inversion and to the presence of several local minima that are caused by correlation among parameters. This is a relevant issue, particularly when the inverse problem to be solved requires the retrieval of a high number of parameters. In such cases, methods to reduce the complexity of the problem are to be sought. In this work, we propose an algorithm suitable to automatically determine which subset of the parameters is mostly relevant in the model, and we apply it to the reconstruction of 2D and 3D scatterers. We compare the results with local sensitivity analysis and with the screening method proposed by Morris.

The development of actinic mask metrology tools represents one of the major challenges to be addressed on the roadmap of extreme ultra violet (EUV) lithography. Technological advancements in EUV lithography result in the possibility to print increasingly fine and highly resolved structures on a silicon wafer, however the presence of fine-scale defects, interspersed in the printable mask layout, may lead to defective wafer prints. Hence the development of actinic methods for review of potential defect sites becomes paramount. Here, we report on a ptychographic algorithm that makes use of prior information about the object to be retrieved, generated by means of rigorous computations, to improve the detectability of defects whose dimensions are of the order of the wavelength. The comprehensive study demonstrates that the inclusion of prior information as a regularizer in the ptychographic optimization problem results in a higher reconstruction quality and an improved robustness to noise with respect to the standard ptychographic iterative engine (PIE). We show that the proposed method decreases the number of scan positions necessary to retrieve an high quality image and relaxes requirements in terms of signal to noise ratio (SNR). The results are further compared with the state-of-art total variation based ptychographic imaging.

EUV lithography is the main candidate for patterning of future technology nodes. Its successful implementation depends on many aspects, among which the availability of actinic mask metrology tools able to inspect the patterned absorber in order to control and monitor the lithographic process. In this work, we perform a simulation study to assess the performance of coherent diffractive imaging (CDI) and related phase retrieval methods for the reconstruction of non-trivially shaped and a-periodic nanostructures from far field intensity data.

Nanoscale non-destructive metrology is a key requirement in several steps of the manufacturing process of modern semiconductor devices. In particular, with the introduction of EUV lithography into the high-volume manufacturing, enabling further shrinking of feature sizes, metrology for future technology nodes will become increasingly challenging. Depending on the specific requirements and constraints of the metrology tasks, the choice of the measurement methods is critical, and sometimes limited. Conventional metrology techniques must be constantly adapted to keep up with the device scaling roadmap. To explore new and easily scalable methods for semiconductor wafer metrology, we developed the EUV reflective grazing-incidence nanoscope (REGINE). REGINE is a lensless nanoscope prototype that combines reflectometry, scatterometry and coherent diffraction imaging (CDI) in a single instrument. Being a lensless imaging system, it is compact, cost-effective, and free of lens-induced aberrations. In this work, we will present the REGINE system and the latest results of reflectometry, scatterometry and EUV pellicle transmission experiments.