Deep UV 1:1 projection lithography utilizing negative resist MRS

A negative deep UV resist Micro Resist for Shorter wavelengths (MRS) is successfully applied to 1:1 projection printing. The MRS is characterized by strong absorption of deep UV light and absence of swelling in the developer. It resolves steep profile images of 1-µm linewidth in 1-µm-thick films. The resist has extremely high sensitivity to deep UV light. Scanning exposure time necessary for a 4-in wafer is about 25 s. The MRS exhibits dry etching resistance superior to that of an AZ-type positive resist. Furthermore, MRS is not adversely affected by reflected light from stepped aluminum surfaces. Application of MRS should open the way to realization of a practical deep UV 1:1 projection lithography featuring high resolution and throughput.     

Estimation of resist profile for line/space patterns using layer-based exposure modeling in electron-beam lithography

One of the essential tasks in the dose control for fabrication of 2-D and 3-D patterns using electron-beam lithography is estimation of remaining resist profiles after development. A conventional approach is to compute the exposure distribution for a target pattern through convolution with the point spread function (PSF) and then obtain the resist profile via simulation of the development process based on the exposure distribution. A new approach which does not require calculation of the exposure distribution and simulation of the resist development is proposed. It utilizes a set of experimental results on which estimation of the resist profile is based, and has a good potential to provide an alternative to the conventional approaches. In this paper, the proposed approach is described in detail along with the results obtained from an extensive simulation and also experiments.     

Electron beam lithography using chemically-amplified resist: Resolution and profile control

The effect of post exposure bake and softbake conditions on the sensitivity of AZPN114 has been investigated experimentally. A bilayer system for undercut structures has been achieved using two layers of AZPN114 with different softbake temperatures. A single layer of AZPN114 has also been used to produce undercut and tailored resist profiles by two different multiple exposure strategies at different beam energies.     

The design challenge in printing devices and circuits: Influence of the orientation of print patterns in inkjet-printed electronics

We present a morphological and electrical analysis of inkjet-printed two-dimensional films of silver nanoparticle inks arranged at different orientation to the raster-scan-based printing process. Different parameters causing morphological and functional irregularities in the inkjet-deposited films as a function of their orientation to the printing process are introduced in detail and the relevance for the field of printed electronics is discussed. Researchers have demonstrated the manufacturing of various microelectronic devices using inkjet printing. Nearly all of the devices are based on simple rectilinear geometries. Usually, these geometries have a preferential orientation that is exactly (i) along the deposition process or exactly (ii) perpendicular to the deposition process. So far, it was assumed that the geometrical and functional characteristics are identically for the both cases. However, we show empirically that this is not the case and help to understand the conditions that lead to the differences.     

Analysis of the properties of germanium/zinc silicate film growth through a simple thermal evaporation technique for hydrogen gas sensing and deep UV photodetector application

Germanium/zinc silicate (Ge/Zn₂SiO₄) thin films were produced in a high-temperature horizontal tube furnace. Structural and optical properties of thin films were investigated by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. Room temperature Raman spectra were also obtained. Nickel (Ni) metal–semiconductor–metal (MSM) contacts were deposited on the Ge/Zn₂SiO₄ thin film by evaporating Ni using an appropriate MSM mask. Corresponding current–voltage characteristics of the Schottky diodes were recorded before and after (2%) hydrogen (H₂) gas exposure with different flow rates. The respectable response and sensitivity of the MSM to H₂ gas heighten the potential interest in future gas sensor devices. The strong photoelectric properties of the MSM in the deep ultraviolet demonstrate that the film contributes to photosensitivity. Therefore, Ge/Zn₂SiO₄ films are potential photodetectors in short wavelength applications.     

Strong compound-risk factors: efficient discovery through emerging patterns and contrast sets.

Odds ratio (OR), relative risk (RR) (risk ratio), and absolute risk reduction (ARR) (risk difference) are biostatistics measurements that are widely used for identifying significant risk factors in dichotomous groups of subjects. In the past, they have often been used to assess simple risk factors. In this paper, we introduce the concept of compound-risk factors to broaden the applicability of these statistical tests for assessing factor interplays. We observe that compound-risk factors with a high risk ratio or a big risk difference have an one-to-one correspondence to strong emerging patterns or strong contrast sets-two types of patterns that have been extensively studied in the data mining field. Such a relationship has been unknown to researchers in the past, and efficient algorithms for discovering strong compound-risk factors have been lacking. In this paper, we propose a theoretical framework and a new algorithm that unify the discovery of compound-risk factors that have a strong OR, risk ratio, or a risk difference. Our method guarantees that all patterns meeting a certain test threshold can be efficiently discovered. Our contribution thus represents the first of its kind in linking the risk ratios and ORs to pattern mining algorithms, making it possible to find compound-risk factors in large-scale data sets. In addition, we show that using compound-risk factors can improve classification accuracy in probabilistic learning algorithms on several disease data sets, because these compound-risk factors capture the interdependency between important data attributes.     

Intelligent laser welding through representation,prediction, and control learning: An architecture with deep neural networks and reinforcement learning

Laser welding is a widely used but complex industrial process. In this work, we propose the use of an integrated machine intelligence architecture to help address the significant control difficulties that prevent laser welding from seeing its full potential in process engineering and production. This architecture combines three contemporary machine learning techniques to allow a laser welding controller to learn and improve in a self-directed manner. As a first contribution of this work, we show how a deep, auto-encoding neural network is capable of extracting salient, low-dimensional features from real high-dimensional laser welding data. As a second contribution and novel integration step, these features are then used as input to a temporal-difference learning algorithm (in this case a general-value-function learner) to acquire important real-time information about the process of laser welding; temporally extended predictions are used in combination with deep learning to directly map sensor data to the final quality of a welding seam. As a third contribution and final part of our proposed architecture, we suggest that deep learning features and general-value-function predictions can be beneficially combined with actor–critic reinforcement learning to learn context-appropriate control policies to govern welding power in real time. Preliminary control results are demonstrated using multiple runs with a laser-welding simulator. The proposed intelligent laser-welding architecture combines representation, prediction, and control learning: three of the main hallmarks of an intelligent system. As such, we suggest that an integration approach like the one described in this work has the capacity to improve laser welding performance without ongoing and time-intensive human assistance. Our architecture therefore promises to address several key requirements of modern industry. To our knowledge, this architecture is the first demonstrated combination of deep learning and general value functions. It also represents the first use of deep learning for laser welding specifically and production engineering in general. We believe that it would be straightforward to adapt our architecture for use in other industrial and production engineering settings.     

Interferential lithography of 1D thin metallic sinusoidal gratings: Accurate control of the profile for azimuthal angular dependent plasmonic effects and applications

Nonlinear processes involved in the manufacture of nominally sinusoidal surface relief diffraction gratings generated by interference lithography can introduce distortions into the profile of these surfaces. Such distortions may dramatically affect both the specular reflectivity and diffracted efficiencies from such a surface [H. Raether, Phys. Thin Film 9 (1977) 145–261]. We shall consider in particular the case of metallic gratings used to investigate plasmonic effects that can be engineered for bio-sensing applications. To investigate these effects, interference lithography (IL) has been used for the generation of profile controlled sinusoidal plasmonic crystals. IL exposure contrast study has been performed to control the amplitude oscillation and the surface roughness quality. Bi-metallic layer of silver and gold have been systematically deposited with different film thicknesses. A comprehensive numerical model that studies the optical coupling to surface plasmon polaritons on Ag/Au gratings has been undertaken for the simulation of the reflectivity and azimuthal angle dependence [Z. Chen, I.R. Hooper, J.R. Sambles, J. Opt. A: Pure Appl. Opt. 10 (1) (2008) 015007]. This computation illustrates the sensitivity of individual features to specific harmonic components of the surface, for surface plasmon resonances recorded in both the zeroth and higher diffracted orders. The roughness surface control after development and after bi-metallic evaporation strongly contributes to tighten the width of the reflectivity peak. Optimization process has shown that for an Ag (37 nm) and Au (7 nm) metallic bilayer, a semi-amplitude of 20 nm provides the best reflectivity.     

1 + 1 >> 2: Dramatically Enhancing the Emission Efficiency of TPE‐Based AIEgens but Keeping their Emission Color through Tailored Alkyl Linkages

Currently, the development of aggregation‐induced emission (AIE) luminogens (AIEgens) has enabled us to “see” never before seen scenery. However, not all AIEgens exhibit the impressive emission efficiency in aggregated states. Moreover, the emission color of AIEgens can be seriously affected when their performance is improved. Therefore, to overcome this limitation, an efficient method is proposed here through the tailored alkyl linkages to greatly improve the emission efficiency of tetraphenylethene (TPE)‐based AIEgens but retain their emission color. Encouragingly, significantly enhanced emission efficiency is achieved with the quantum yield up to 68.19% and 65.20% for BTPE‐C4 and BTPE‐C8, respectively, in contrast to that of TPE (25.32%), demonstrating the proverb that one plus one is much larger than two (1 + 1 >> 2). Interestingly, when alkyl linkages in skeletons are fine‐tuned, self‐assembled nanorods, nanosheets, and nanofibers are successfully achieved for BTPE‐C1, BTPE‐C4, and BTPE‐C8 in tetrahydrofuran and water system. Also, these developed emissive AIEgens not only exhibit impressive response to the environmental stimuli of mechanical force, viscosity, temperature, and light, but can also be used to dynamically monitor and control the phase‐separated morphology in polymeric blends.