THz Frequency Selective Surface Filters for Earth Observation Remote Sensing Instruments

The purpose of this paper is to review recent developments in the design and fabrication of Frequency Selective Surfaces (FSS) which operate above 300 GHz. These structures act as free space electromagnetic filters and as such provide passive remote sensing instruments with multispectral capability by separating the scene radiation into separate frequency channels. Significant advances in computational electromagnetics, precision micromachining technology and metrology have been employed to create state of the art FSS which enable high sensitivity receivers to detect weak molecular emissions at THz wavelengths. This new class of quasi-optical filter exhibits an insertion loss

Whole wafer magnetostriction metrology for magnetic films and multilayers

The requirements for metrology of magnetostriction in complex multilayers and on whole wafers present challenges. An elegant technique based on radius of curvature deformation of whole wafers in a commercial metrology tool is described. The method is based on the Villari effect through application of strain to a film by introducing a radius of curvature. Strain can be applied tensilely and compressively depending on the material. The design, while implemented on 3'' wafers, is scalable. The approach removes effects arising from any shape anisotropy that occurs with smaller samples, which can lead to a change in magnetic response. From the change in the magnetic anisotropy as a function of the radius, saturation magnetostriction ?s can be determined. Dependence on film composition and film thickness was studied to validate the radius of curvature approach with other techniques. ?s decreases from positive values to negative values through an increase in Ni concentration around the permalloy composition, and ?s also increases with a decrease in film thickness, in full agreement with previous reports. We extend the technique by demonstrating the technique applied to a multi-layered structure. These results verify the validity of the method and are an important step to facilitate further work in understanding how manipulation of multilayered films can offer tailored magnetostriction.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Recent developments in X-UV optics and X-UV diagnostics

Metrology of XUV beams (X-ray lasers, high-harmonic generation and VUV free-electron lasers) is of crucial importance for the development of applications. We have thus developed several new optical systems enabling us to measure the optical properties of XUV beams. By use of a Michelson interferometer working as a Fourier-transform spectrometer, the line shapes of different X-ray lasers have been measured with a very high accuracy (Deltalambda/lambdasimilar to10(-6)). Achievement of the first XUV wavefront sensor has enabled us to measure the beam quality of laser-pumped as well as discharge-pumped X-ray lasers. A capillary discharge X-ray laser has demonstrated a very good wavefront allowing us to achieve an intensity as high as 3x10(14) W cm(-2) by focusing with a f=5 cm mirror. The sensor accuracy has been measured using a calibrated spherical wave generated by diffraction. The accuracy has been estimated to be as good as lambda/120 at 13 nm. Commercial developments are underway. At Laboratoire d'Optique Appliquee, we are setting up a new beamline based on high-harmonic generation in order to start the femtosecond, coherent XUV optic .

Instrumentation for diagnostics and control of laser-accelerated proton (ion) beams

Suitable instrumentation for laser-accelerated proton (ion) beams is critical for development of integrated, laser-driven ion accelerator systems. Instrumentation aimed at beam diagnostics and control must be applied to the driving laser pulse, the laser-plasma that forms at the target and the emergent proton (ion) bunch in a correlated way to develop these novel accelerators. This report is a brief overview of established diagnostic techniques and new developments based on material presented at the first workshop on 'Instrumentation for Diagnostics and Control of Laser-accelerated Proton (Ion) Beams' in Abingdon, UK. It includes radiochromic film (RCF), image plates (IP), micro-channel plates (MCP), Thomson spectrometers, prompt inline scintillators, time and space-resolved interferometry (TASRI) and nuclear activation schemes. Repetition-rated instrumentation requirements for target metrology are also addressed. (C) 2013 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Picosecond metrology of laser-driven proton bursts

Tracking primary radiation-induced processes in matter requires ultrafast sources and high precision timing. While compact laser-driven ion accelerators are seeding the development of novel high instantaneous flux applications, combining the ultrashort ion and laser pulse durations with their inherent synchronicity to trace the real-time evolution of initial damage events has yet to be realized. Here we report on the absolute measurement of proton bursts as short as 3.5±0.7 ps from laser solid target interactions for this purpose. Our results verify that laser-driven ion acceleration can deliver interaction times over a factor of hundred shorter than those of state-of-the-art accelerators optimized for high instantaneous flux. Furthermore, these observations draw ion interaction physics into the field of ultrafast science, opening the opportunity for quantitative comparison with both numerical modelling and the adjacent fields of ultrafast electron and photon interactions in matter.