This guest editorial introduces the Special Section on Control of Integrated Circuit Patterning Variance, Part 4: Placement and Critical Dimension, Edge to Edge Overlay.

Background: The mathematical equations that explain overlay error of multiple-exposure patterning schemes have not been fully described in the literature and some commonly accepted methods lead to inaccurate estimated and/or measured overlay error. Aims: Develop the proper mathematical framework, using a first principles statistical approach, so that engineers using multiple-exposure patterning can determine the overlay impact and overlay controls needed. Alert patterning community that grouped overlay metrology of multiple-exposures undermeasures the true overlay error. Approach: Use image placement error and population-based statistics to enable a mathematical framework to be established that predicts the actual overlay error for an overlaying pattern that minimizes overlay error back to a pattern that is patterned with multiple-exposure patterning. Results: The overlay error between two patterns is usually less than the root sum square of the two overlay error values of the patterns individually measured to a common prior pattern. Overlay error for a pattern minimizing back to multiple-prior patterns increases quickly as systematic overlay error between the prior patterns increases. Conclusions: Controlling systematic overlay error between patterns of a multipatterned layer is important for subsequent patterns that need to minimize overlay error back to the composite multipatterned layer. The ratio between the overlay error determined with metrology and true overlay can be calculated.

Background: The continuous scaling of integrated circuit requires not only a very good control of the device critical dimensions but also a very accurate control of the device overlay between layers to achieve satisfactory yields. These two critical factors can be combined to a single metric called interlayer edge placement error (EPE_interlayer) that quantifies the process margin necessary to keep a safe separation, extension, or overlap between the edges of a pattern in one layer with respect to another pattern in a second layer. Aim: The purpose of this work is to characterize with scanning electron microscopy (SEM), the EPE_interlayer and overlay variances of complex contact shapes relative to a poly layer to assess the contributions of the systematic and random EPE_interlayer. Approach: SEM images of a few etched patterns were recorded sequentially for both contact and poly features at the same locations on the wafer. Then, SEM contours were extracted, aligned, and overlapped to derive EPE_interlayer. One experiment was focusing on intrawafer EPE_interlayer characterization whereas another was studying more specifically intrafield overlay variations. For the latter experiment, systematic overlay errors were added to facilitate the comparison of the SEM-based method with respect to a reference image-based overlay (IBO) method. Results: The earliest direct metrology of EPE_interlayer and overlay in device enabled by this work shows a very high variability of EPE_interlayer and overlay errors across wafer and across field. Conclusions: By directly measuring the EPE_interlayer on devices that are not accessible by the standard method (optical IBO on OL structures), we showed the feasibility of this metrology and observed more dimensional variance in devices than recognized with IBO, thereby enabling better control of device pattern variation.

The influence of the e-beam aperture angle on the critical dimensions (CD)-scanning electron microscope measurements for a high aspect ratio (AR) structure is investigated. The Monte Carlo simulator JMONSEL is used for evaluating the measurement sensitivity to the variation in the bottom CD. The aperture angle of the primary electron greatly influences the measurement precision of the bottom CD in the high AR structure. Then, we applied an energy-angular selective detection technique to the Monte Carlo simulation results and found that the measurement sensitivity for the large aperture angle was improved. In addition, the experimental results are qualitatively consistent with the results of the Monte Carlo simulation. These results indicate that the energy-angular selective detection technique is effective for improving the measurement resolution of CD at trench bottom of a high AR structure and the technique is also useful for the overlay measurement during after-etch inspection.

A methodology to evaluate the electrical contact between nanowire (NW) and source/drain in NW FETs was investigated with SEM voltage contrast (VC). The electrical defects are robustly detected by VC. The validity of the inspection result was verified by transmission electron microscope (TEM) physical observations. Moreover, estimation of the parasitic resistance and capacitance was achieved from the quantitative analysis of VC images, which are acquired with different scan conditions of an electron beam (EB). A model considering the dynamics of EB-induced charging was proposed to calculate the VC. The resistance and capacitance can be determined by comparing the model-based VC with experimentally obtained VC. Quantitative estimation of resistance and capacitance would be valuable not only for more accurate inspection but also for identification of the defect point.

Overlay control has been one of the most critical issues for manufacturing of leading edge semiconductor devices. Introduction of the double patterning process requires stringent overlay control. Conventional optical overlay (Opt-OL) metrology has technical challenges with measurement robustness, solving overlay discrepancy between overlay mark and device pattern, and measuring smaller marks laid out in large numbers within the die accurately for high-order correction. In contrast, scanning electron microscope-based overlay (SEM-OL) metrology can directly measure both overlay targets and actual devices or device-like structures on processed wafers with high spatial resolution. It can be used for reference metrology and optimization of Opt-OL measurement conditions. SEM-OL uses small structures, including actual device patterns, which allows insertion of many SEM-OL targets across a die. Precise overlay distribution can be measured using dedicated SEM-OL mark, improving measurement accuracy and repeatability. To extend SEM-OL capability, we have been developing SEM-OL techniques that can measure not only surface patterns by critical dimension SEM but also buried patterns for leading edge device processes. There are two techniques to detect buried patterns. One is to use high-acceleration voltage SEM, which detects backscattering electron emphasizing material contrast. It has been adopted for overlay measurements for memory and logic devices at after-etch inspection or even after-develop inspection. The other is to utilize charging effect, which reflects voltage contrast at the surface depending on the material properties of underneath structure. SEM-OL measurement using transient voltage contrast has been developed and its capability of overlay measurement has been proven. An overlay measurement algorithm using template matching method has been developed and was applied to dynamic random access memory (DRAM) process monitor in manufacturing. In order to extend SEM-OL metrology to beyond 3-nm node logic and cutting-edge DRAM devices (half pitch = 14 nm), we are improving measurement precision of detecting buried patterns and measurement throughput by developing optimized SEM-OL mark.

With continuous downscaling of feature sizes, potentially problematic patterns (hotspots) have become a major issue in generation of optimized mask design for better printability. The lithography process sensitive patterns in a design lead to degradation of both electrical performance and manufacturing yield of the integrated circuit. Due to sequential flow of very large-scale integration (VLSI) design and manufacturing, missing any hotspot has an adverse impact on product turnaround time and cost. The lithographic samples are generally defined using a combination of continuous variables (to represent aerial image and pattern density) and categorical variables (to represent allowed layout design rules). The conventional hotspot classification techniques suffer from suboptimum performance due to their inability to efficiently represent and use the above-mentioned feature metrics. In general, the number of hotspots in the lithographic data is much less compared to the total number of patterns in a full-chip design. It makes the input data imbalanced and adds additional difficulties in the decision making processes. We present a robust technique to detect the process sensitive patterns using random forest-based machine learning technique. The emphasis is put on the layout features extraction techniques to improve the performance of the proposed approach. The simulation results show that the patterns susceptible to variations under different dose and focus conditions undergo a drastic change in their aerial image characteristics even when the geometry is varied by a very small margin. We observed from our analysis that the minimum number of false negatives can be achieved with reasonable increase in the number false positives. Moreover, compared to conventional hotspot classification techniques, we are able to achieve a very low percentage of false negatives with a binary classifier trained on an imbalanced dataset. Another key observation from our analysis is that the random forest method can obtain the most representative heuristics required to define categories from the lithographic datasets with continuous and categorical variables. In addition, our proposed approach can easily be integrated with commercially available electronic design automation tools and in-house design simulators to make the process flow viable in terms of a business perspective.

Background: Deep x-ray lithography using synchrotron radiation is a prominent technique in the fabrication of high aspect ratio microstructures. The minimum lateral dimensions producible are limited by the primary dose distribution and secondary effects (Fresnel diffraction, secondary electrons scattering, etc.) during exposure. Aim: The influence of secondary radiation effects on the fabrication of high aspect ratio microstructures with submicrometer lateral dimension by deep x-ray lithography is characterized. Approach: The microstructures under investigation are one-dimensional gratings. The influence of secondary effects on structural dimension is simulated and compared to the experimental results. The quality criteria and possible defects arising in experiments highlight the importance of the mechanical stability of the photoresist. Results: From the simulation results, the minimum period of microstructures that can be produced is about 600 nm. Experimentally, microstructures with 1.2  μm minimum period (resist width of ∼700  nm) and height of ∼10  μm could be fabricated. Conclusions: Simulation results show the feasibility for fabricating gratings with a period less than 1  μm. To achieve these values also in experiment, it is necessary to increase the mechanical stability of the high aspect lamellae. The outcome of these results allows one to reduce the expensive and lengthy product development cycle.

Background: As semiconductor technologies continue to shrink, the growth in the number of process variables and combined effects tighten the overall process window, which leads to a more serious yield loss. Yield cannot be totally guaranteed by design rule check and verifications of optical proximity correction, due to complex process variations. The joint effects from unreasonable designs and unstable control of critical dimensions and overlay mainly contribute to the formation of bridging defects in critical interconnect layers. Aim: Our paper puts forward a model to detect the potential bridging region and predicts the corresponding failure probability under a litho-etch-litho-etch process. Approach: The proposed model is based on input error sources from variations of lithography and etch processes. In this scheme, bridging is expected when the minimum space of simulated postetch contours within a specific range is smaller than a user-defined bridging threshold. Gaussian distribution characteristics of line edge roughness (LER) and overlay are considered in the proposed model. Moreover, the proposed model provides meaningful guidelines for bridging prediction with the use of process variation bands. Results: The experiment results indicate consistency and validity of theoretical derivation of the proposed model. The concrete impacts of LER and overlay on the model have been quantitatively analyzed as well. Conclusions: According to the predicted probabilities, the model can early discover potential bridging defects quantitatively by considering the statistical properties of process variations with very few calculations and can give a ranking of failure severity as a decision foundation for design rule optimization.

We describe a methodology for nanoscale molecular analysis and present its capabilities. The analysis method is based on secondary-ion mass spectrometry with gold nanoparticles (e.g., Au4004+). The methodology presented has unique features that enable nanoscale molecular analysis, namely the method of acquiring the mass spectrum and the nature of the impacting projectile. In the method, a sequence of individual gold nanoparticles ( Au4004+) is used to bombard the sample; each impact results in ion emission from an area ∼10–20  nm in diameter. For each of impact of Au4004+, the emitted ions are mass analyzed by time-of-flight mass spectrometry, detected and stored together in one mass spectrum prior to the arrival of the subsequent projectile. Each mass spectrum contains elements and molecules, which are colocalized within ∼10 to 20 nm of one another. Examination of the coemitted ions allows us to test the molecular homogeneity and chemical composition at the nanoscale. We applied this method to a chemically amplified resist before and after exposure and development. After development the method was used to chemically characterize defect sites that were not removed by the developing solution.

Background: Standard patterned sample with programed defects (PDs) is effective to evaluate the tool performance of pattern inspection system, but the fabrication of such standard sample, having large area dense patterns with PDs suitable for the evaluation of sub-7-nm node, is difficult. Aim: The goal of this study is to fabricate a standard sample to evaluate the performance of inspection tool for below 7-nm nodes. Approach: We use electron beam lithography with an acceleration voltage of 130 keV to fabricate standard sample. Results: We form large area dense sub-16-nm half pitch (hp) line and space (LS) patterns with PDs on 300-mm-Si-wafers, and 10- to 7-nm hp LS patterns on a 100-mm-Si wafer. Approximately 5-nm PDs with shapes including protrusions, intrusions, bridges, and openings are formed without additional defects. Moreover, pattern-etched Si wafers with 16- to 12-nm hp LS are successfully fabricated. A 100-mm-wafer with patterns is mounted into a 300-mm-Si wafer. Conclusions: The acceleration voltage of 130 keV is sufficient for the fabrication of large area dense pattern with PDs suitable for the evaluation of sub-7-nm node. Moreover, the fabricated standard wafers are useful to evaluate the tool performance of the inspection system for 300-mm wafer fabrication.

We propose the use of deep supervised learning for the estimation of line edge roughness (LER) and line width roughness (LWR) in low-dose scanning electron microscope (SEM) images. We simulate a supervised learning dataset of 100,800 SEM rough line images constructed by means of the Thorsos method and the ARTIMAGEN library developed by the National Institute of Standards and Technology. We also devise two separate deep convolutional neural networks called SEMNet and EDGENet, each of which has 17 convolutional layers, 16 batch normalization layers, and 16 dropout layers. SEMNet performs the Poisson denoising of SEM images, and it is trained with a dataset of simulated noisy-original SEM image pairs. EDGENet directly estimates the edge geometries from noisy SEM images, and it is trained with a dataset of simulated noisy SEM image-edge array pairs. SEMNet achieved considerable improvements in peak signal-to-noise ratio as well as the best LER/LWR estimation accuracy compared with standard image denoisers. EDGENet offers excellent LER and LWR estimation as well as roughness spectrum estimation.

Projection lithography using extreme ultraviolet (EUV) light at 13.5-nm wavelength will be applied to the production of integrated circuits below 7-nm design rules. In pursuit of further miniaturization, however, stochastic pattern defect problems have arisen, and monitoring such defect generation probabilities in extremely low range (<^{10  −  10}) is indispensable. We discuss a method for predicting stochastic defect probabilities from a histogram of feature sizes for patterns several orders of magnitude fewer than the number of features to inspect. Based on our previously introduced probabilistic model of stochastic pattern defect, the defect probability is expressed as the product sum of the probability for edge position and the probability that film defect covers the area between edges, and we describe the latter as a function of edge position. The defect probabilities in the order between ^{10  −  7} and ^{10  −  5} were predicted from 10⁵ measurement data for real EUV-exposed wafers, suggesting the effectiveness of the model and its potential for defect inspection.

Extreme ultraviolet (EUV) lithography is one of the most promising printing techniques for high-volume semiconductor manufacturing at the 14-nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials, such as the exposure-dose sensitivity or the line-width roughness, continue to impede its full adoption into industrial nanofab facilities. Metrology tools are required to address these challenges by helping to assess the impact of the EUV materials’ properties and processing conditions along different steps of the nanofabrication process. We apply the resonant soft x-ray scattering (RSoXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon K-edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with subnanometer precision. Critical-dimension grazing-incidence small-angle x-ray scattering is then performed at energies where the contrast is maximized, enabling the reconstruction of the three-dimensional shape of the latent image. We demonstrate the potential of RSoXS to provide a high-resolution height-sensitive profile of patterned EUV resists, which will help in quantifying the evolution of critical features, such as the line-edge roughness, at a key step of the nanofabrication process.

We present a simple method of deep anisotropic etching of silicon up to 400  μm with nearly vertical sidewall profile for thermopile devices. The method is based on the time-multiplexed etching which is a modified case of the Bosch deep reactive ion etching process. This process is mainly adjusted by chuck power and it is divided into three steps which are zero bias deposition step, high bias polymer removing step, and low bias silicon etching step. Compared with the standard Bosch process, this modified strategy shows advantage of etch rate and selectivity.

Background: Microelectromechanical systems are now one of the fastest growing engineering fields. We introduce a static gas sensor based on PolyMUMPs parallel plate actuators. The sensor exploits the pull-in phenomenon of the parallel plate actuators. The target gas is hydrogen sulfide (H₂S), which is a toxic gas and popular in laboratories, factories, and petroleum industry. Aim: Reach a gas sensor in which the gas can easily be detected by a simple electronic circuit using new polymer combinations. Approach: Two concentrations of H₂S (40 and 100 ppm) are used to test the ability of the sensor to capture the molecules of the gas. Gas injection process is done in a gas chamber and at ambient conditions (ambient temperature and pressure). Results: The two concentrations were successfully detected by the sensor and the electronic circuit verified the pull-in of the sensor. That was achieved using two different polymers (polypyrrole polymer and copper oxide–tin oxide/polypyrrole). Conclusions: The sensor successively detects H₂S gas with 40 and 100 ppm concentrations. Verifying the pull-in of the sensor using a simple detection circuit could help in quickly moving those sensors from prototype to product.

A new electrostatic actuator for a double-layer deformable mirror (DM) with continuous phase sheet is presented. The electrostatic actuator employs an upper electrode consisting of multiple Archimedean spiral spring arms supporting a small mass central attachment point and is capable of separating the parameters of actuation voltage, spring constant, and resonance. The feature reduces the complexity in the designing of a double-layer DM with continuous phase sheet, which is widely employed in the adaptive optics area of astronomy. The fabricated device has a diameter of 1250  μm with four single-crystal silicon spiral arms each having a length of 6030  μm, thickness of 15  μm, and width of 30  μm. The actuator was simulated and tested to have a resonant frequency above 2600 Hz and was simulated to be capable of reaching ∼5-μm displacement with a drive voltage of <30  V with an actuator–electrode separation of 15  μm, based on the performance parameter found experimentally.