Water Immersion Optical Lithography for the 45nm Node

It is possible to extend optical lithography by using immersion imaging methods. Historically, the application of immersion optics to microlithography has not been seriously pursued because of the alternative solutions available. As the challenges of shorter wavelength become increasingly difficult, immersion imaging becomes more feasible. We present results from research into 193nm excimer laser immersion lithography at extreme propagation angles (such as those produces with strong OAI and PSM). This is being carried out in a fluid that is most compatible in a manufacturable process, namely water. By designing a system around the optical properties of water, we are able to image with wavelengths down to 193nm. Measured absorption is below 0.50 cm at 185nm and below 0.05 cm\u27 at 193nm. Furthermore, through the development of oblique angle imaging, numerical apertures approaching 1.0 in air and 1.44 in water are feasible. The refractive index of water at 193nm (1.44) allows for exploration of the following: 1. k1 values approaching 0. 17 and optical lithography approaching 35nm. 2. Polarization effects at oblique angles (extreme NA). 3. Immersion and photoresist interactions with polarization. 4. Immersion fluid composition, temperature, flow, and micro-bubble influence on optical properties (index, absorption, aberration, birefringence). 5. Mechanical requirements for imaging, scanning, and wafer transport in a water media. 6. Synthesizing conventional projection imaging via interferometric imaging

Using Virtual Markets to Program Global Behavior in Sensor Networks

This paper presents market-based macroprogramming (MBM), a new paradigm for achieving globally efficient behavior in sensor networks. Rather than programming the individual, low-level behaviors of sensor nodes, MBM defines a virtual market where nodes sell "actions" (such as taking a sensor reading or aggregating data) in response to global price information. Nodes take actions to maximize their own utility, subject to energy budget constraints. The behavior of the network is determined by adjusting the price vectors for each action, rather than by directly specifying local node actions, resulting in a globally efficient allocation of network resources. We present the market-based macro-programming paradigm, as well as several experiments demonstrating its value for a sensor network vehicle tracking application.Engineering and Applied Science

EVALUATION OF THE INTRINSIC AND EXTRINSIC FRACTURE BEHAVIOR OF IRON ALUMINIDES

Comparative finite element modeling simulations of initial intergranular fracture of two iron aluminides (FA186 and FA189) were carried out to study the intrinsic and extrinsic fracture behavior of the alloys as related to hydrogen embrittlement. The computational simulations involved sequentially-coupled stress and mass-diffusion analyses to determine the stress/strain distribution and the extent of hydrogen concentration at the crack tip region. Simulations of initial intergranular fracture of the two alloys under either air or vacuum conditions were conducted. With judicious selection of grain boundary failure strains for each alloy and assumed material degradation at hydrogen diffusion zone, the numerical results agree well with previous experimental test results. We have considered the various methods by which the thermal expansion of Fe{sub 3}Al can be modeled. As a matter of practicality, we have started with a conceptually simple continuum medium modeling, which we have used in initial calculations reported here, despite its limitations in neglecting the effects of optical phonons. This makes the results increasingly suspect for temperatures above the Debye temperature. However, the results we obtain are surprisingly good considering this important limitation. Nevertheless, we regard these results as being suspect. Therefore, in addition, we discuss a wholly new ab-initio-based method which is both more accurate (preserves the ab-initio-generated information) and computationally more efficient. This method can directly transform the all-electron ab initio electronic structure results of the full-potential LMTO electronic structure behavior, computationally provided in reciprocal space, to the real space representation needed for the thermal expansion modeling. An increase of computational speed, use of larger supercells, and more efficient calculations, can all be achieved by using real space (tight-binding (TB)) calculations. The TB parameters are obtained from direct Fourier transform of the matrix elements in momentum space for a specific structure and specific lattice constant. The parameters that may change significantly are the onsite parameters, which depend on the onsite electron density. To make a usable look-up table, good for variable lattice constant in the same structure, one can perform several runs with different lattice constants and obtain a fitting function of the onsite parameter as a function of lattice constant, for each orbital in each atom. We are at present implementing this method for initial application to Fe{sub 3}Al before proceeding to a study of molybdenum silicide systems

Europe’s first and last field trial of gene-edited plants?

On 5 June this year the first field trial of a CRISPR-Cas-9 gene-edited crop began at Rothamsted Research in the UK, having been approved by the UK Department for Environment, Food & Rural Affairs. However, in late July 2018, after the trial had started, the European Court of Justice ruled that techniques such as gene editing fall within the European Union's 2001 GMO directive, meaning that our gene-edited Camelina plants should be considered as genetically modified (GM). Here we describe our experience of running this trial and the legal transformation of our plants. We also consider the future of European plant research using gene-editing techniques, which now fall under the burden of GM regulation, and how this will likely impede translation of publicly funded basic researc

Multi-Task Imitation Learning for Linear Dynamical Systems

We study representation learning for efficient imitation learning over linear systems. In particular, we consider a setting where learning is split into two phases: (a) a pre-training step where a shared $k$-dimensional representation is learned from $H$ source policies, and (b) a target policy fine-tuning step where the learned representation is used to parameterize the policy class. We find that the imitation gap over trajectories generated by the learned target policy is bounded by $\tilde{O}\left( \frac{k n_x}{HN_{\mathrm{shared}}} + \frac{k n_u}{N_{\mathrm{target}}}\right)$, where $n_x > k$ is the state dimension, $n_u$ is the input dimension, $N_{\mathrm{shared}}$ denotes the total amount of data collected for each policy during representation learning, and $N_{\mathrm{target}}$ is the amount of target task data. This result formalizes the intuition that aggregating data across related tasks to learn a representation can significantly improve the sample efficiency of learning a target task. The trends suggested by this bound are corroborated in simulation.Comment: Appeared in L4DC 2023. V3: corrected typo in assumption

Immersion Microlithography at 193 nm with a Talbot Prism Interferometer

A Talbot interference immersion lithography system that uses a compact prism is presented. The use of a compact prism allows the formation of a fluid layer between the optics and the image plane, enhancing the resolution. The reduced dimensions of the system alleviate coherence requirements placed on the source, allowing the use of a compact ArF excimer laser. Photoresist patterns with a half-pitch of 45 nm were formed at an effective NA of 1.05. In addition, a variable-NA immersion interference system was used to achieve an effective NA of 1.25. The smallest half-pitch of the photoresist pattern produced with this system was 38 nm