The co-editors-in-chief talk about the coming split of JM3 into two journals.

Background: Scatterometry is a fast, indirect, and nondestructive optical method for quality control in the production of lithography masks. To solve the inverse problem in compliance with the upcoming need for improved accuracy, a computationally expensive forward model that maps geometry parameters to diffracted light intensities has to be defined.

Aim: To quantify the uncertainties in the reconstruction of the geometry parameters, a fast-to-evaluate surrogate for the forward model has to be introduced.

Approach: We use a nonintrusive polynomial chaos-based approximation of the forward model, which increases speed and thus enables the exploration of the posterior through direct Bayesian inference. In addition, this surrogate allows for a global sensitivity analysis at no additional computational overhead.

Results: This approach yields information about the complete distribution of the geometry parameters of a silicon line grating, which in return allows for quantifying the reconstruction uncertainties in the form of means, variances, and higher order moments of the parameters.

Conclusions: The use of a polynomial chaos surrogate allows for quantifying both parameter influences and reconstruction uncertainties. This approach is easy to use since no adaptation of the expensive forward model is required.

Background: In-line metrology for three-dimensional (3D) profiling high-aspect-ratio (HAR) features is highly important for manufacturing semiconductor devices, particularly for memory devices, such as 3D NAND and DRAM.

Aim: Our purpose was to obtain the cross-sectional profiles of the HAR features from top-view critical dimension scanning electron microscopy (CD-SEM) images.

Approach: Based on Monte Carlo simulation results, we proposed a method for 3D profiling of HAR features using backscattered electron (BSE) signal intensities. Several kinds of HAR holes with different taper angles and bowing geometries were fabricated. High-voltage CD-SEM was used for experiments to determine the feasibility of our approach.

Results: Using the BSE line-profile, we constructed cross sections of the taper holes and estimated sidewall angles (SWAs), which were approximately the same as those observed using field-emission scanning electron microscopy (FE-SEM). The constructed cross sections of the bowing holes and the trends of the geometric variance, which were estimated by the middle CD and its depth, were consistent with the cross sections observed by FE-SEM.

Conclusions: The results demonstrate that the variation in the HAR holes, such as SWA and bowing geometry, can be measured and monitored using the BSE images.

We continue our work on the physics of mask-topography-induced phase effects in imaging using extreme ultraviolet (EUV) lithography, and specifically how these effects can be mitigated by alternative mask absorbers. We present a semianalytical model to calculate the mask-topography-induced phase offset and study its trend throughout the entire material space at 13.5-nm wavelength. We demonstrate that the model is in good agreement with 3D rigorous simulations. Using the model, we explain why the previously demonstrated phase shift close to 1.2π works optimally for EUV imaging. We show a low refractive index mask absorber (n  <  0.91) is crucial for good mask 3D mitigation. We demonstrate the importance of mask bias and incident angle for imaging with an optimized attenuated phase-shift mask (PSM), which makes good source-mask optimization indispensable. We present the lithographic performance of alternative mask absorbers including a high-k mask, and a low- and high-transmission attenuated PSM for a few basic use cases, confirming the lithographic gain that can be obtained by mask-absorber optimization.

Background: Microfluidics has been widely used in the biological and medical fields, and polymers are the most widely used materials in microfluidics at present due to their low cost and ease of processing. Both thermoplastics and thermosets were used as the bulk materials in microfluidics. The third option of a material with both advantages from thermoplastics and thermosets will be of great significance.

Aim: We try to establish a low cost and rapid fabrication approach for thermoplastic polyurethane (TPU)-based microfluidics. Several demonstrations were also provided with the proposed fabrication method for TPU-based microfluidics.

Approach: A CO₂ laser ablation instrument was used for the fabrication of the TPU-based microfluidic devices. The width and depth of microchannels fabricated with various laser scan speeds and energies were studied in detail. For sealing the fabricated channels, a thermal fusion bonding method was also proposed with the bonding strength testing. Several types of the most commonly used microfluidic chips were fabricated for demonstration of the proposed fabrication method.

Results: A comprehensive fabrication approach for TPU-based microfluidic devices was achieved. A series of microfluidic chips were designed, fabricated, and tested.

Conclusions: TPU-based microfluidics is achievable and could be used as an alternative material for polydimethylsiloxane or thermoplastics for the fabrication of microfluidic devices. The proposed method could have broad potential applications in biological and chemical fields.

Background: Stochastic defects are becoming major concern in the future extreme ultraviolet (EUV) lithography as their probability P_d exponentially increases with decreasing feature size and is highly sensitive to variations in process/mask conditions. Photon shot-noise and discrete/probabilistic nature of materials have been blamed as their causes.

Aim: We introduce models for relating P_d to photon and resist statistics under various exposures and material conditions and analyze their impact in future EUV lithography.

Approach: Three-dimensional reaction distribution is calculated by a fully coupled Monte Carlo simulation including discrete photon, photoelectron scattering, and resist stochastics. Then probability models predict P_d from statistical data extracted from Monte Carlo results.

Results: Stochastic defect generation is enhanced by cascade and/or cluster of correlated reactions among nearby polymers/molecules due to secondary electrons (SE)/acid diffusion and SEs generated along scattered photoelectron trajectories. P_d decreases with increasing reaction density, suppressing effective image blur, and introducing quenchers, where reaction density is limited by SE, photoacid generator, and reaction site. Defect probability increases with decreasing target size for the same k1-factor, while strongly dependent on image slope and defocus.

Conclusions: Our analyses suggest that applying EUV lithography to smaller target requires careful material choice, extremely precise process control, and further EUV power enhancement.

Defects in semiconductor processes can limit yield, increase overall production cost, and also lead to time-dependent critical component failures. Current state-of-the-art optical and electron beam (EB) inspection systems rely on rule-based techniques for defect detection and classification, which are usually rigid in their comparative processes. This rigidity limits overall capability and increases relative engineering time to classify nuisance defects. This is further challenged due to shrinkage of pattern dimensions for advanced nodes. We propose a deep learning-based workflow that circumvents these challenges and enables accurate defect detection, classification, and localization in a unified framework. In particular, we train convolutional neural network-based models using high-resolution EB images of wafers patterned with various types of intentional defects and achieve robust defect detection and classification performance. Furthermore, we generate class activation maps to demonstrate defect localization capability of the model “without” explicitly training it with defect location information. To understand the underlying decision-making process of these deep models, we analyze the learned filters in pixel space and Fourier space and interpret the various operations at different layers. We achieve high sensitivity (97%) and specificity (100%) along with rapid and accurate defect localization. We also test performance of the proposed workflow on images from two distinct patterns and find that in order to retain high accuracy a modest level of retraining is necessary.

A single-chip microelectromechanical system (MEMS) capacitive microphone is designed and modeled. The mechanical model of the structure is extracted and the mathematical equations for a description of the microphone behavior are obtained. Then the proposed microphone characteristics are considered. In this structure, by adding Z-shape arms around the diaphragm, diaphragm hardness is decreased and diaphragm displacement becomes uniform. The sensitivity and the pull-in voltage are improved despite the decreasing size. The perforated diaphragm of this microphone is supported by Z-shape arms at its four corners. These arms around the diaphragm decrease the stiffness and air damping of the microphone. The behavior of this microphone is also analyzed by the finite element method. The structure has a diaphragm thickness of 2  μm, a diaphragm size of 0.32  ×  0.32  mm², an air gap of 2  μm, and a highly doped monocrystalline silicon wafer as a backplate. The proposed microphone is simulated with IntelliSuite software. According to the results, the new microphone has a sensitivity of 14.245  mV  /  Pa and a pull-in voltage of 5.83 V. The results show that the proposed MEMS capacitive microphone is one of the best structures in performance. The obtained mathematical equations for description of the microphone’s behavior have good agreement with the simulation results.