Recurrent Neural Network for Predicting Dielectric Mirror Reflectivity

https://doi.org/10.20965/jaciii.2019.p1012 Ohkubo, T. et al.

Paper: Recurrent Neural Network for Predicting Dielectric Mirror Reﬂectivity

Tomomasa Ohkubo∗,†, Ei-ichi Matsunaga∗, Junji Kawanaka∗∗, Takahisa Jitsuno∗∗, Shinji Motokoshi∗∗∗, and Kunio Yoshida∗∗

∗Tokyo University of Technology 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan E-mail: [email protected] ∗∗Institute of Laser Engineering, Osaka University 2-6 Yamadaoka, Suita, Osaka 565-0871, Japan ∗∗∗Institute for Laser Technology 1-8-4 Utsubo-honmachi, Nishi-ku, Osaka 550-0004, Japan †Corresponding author [Received February 19, 2019; accepted June 25, 2019]

Optical devices often achieve their maximum effective- 1. Introduction ness by using dielectric mirrors; however, their design techniques depend on expert knowledge in spec- Advances in optics, such as in the field of lasers, ifying the mirror properties. This expertise can also have been remarkable and dielectric multilayer films have be achieved by machine learning, although it is not played a significant role. In particular, the dielectric mir- clear what kind of neural network would be effective ror has met the demands of those ongoing developments; for learning about dielectric mirrors. In this paper, it is one of the most important components and is used we clarify that the recurrent neural network (RNN) is in many optical devices. A dielectric mirror functions by an effective approach to machine-learning for dielec- interference between light waves reflected from the inter- tric mirror properties. The relation between the thick- faces between its films, and has various characteristics de- ness distribution of the mirror’s multiple film layers pending on the interior structure. and the average reflectivity in the target wavelength A dielectric mirror is made up of multiple layers of region is used as the indicator in this study. Reflection dielectric materials having different refractive indexes. from the dielectric multilayer film results from the se- Therefore, we can control factors such as the overall re- quence of interfering reflections from the boundaries flectivity and the phase delay of individual reflected rays between film layers. Therefore, the RNN, which is usu- inside a dielectric mirror by changing the thickness of ally used for sequential data, is effective to learn the re- each film. For example, to construct a band pass filter, lationship between average reflectivity and the thick- i.e. a highly reflective mirror that passes only a particular ness of individual film layers in a dielectric mirror. We set of wavelengths, an anti-reflection coating and high- found that a RNN can predict its average reflectivity reflection coating are made of dielectric films. with a mean squared error (MSE) less than 10−4 from To design a general dielectric mirror, nonlinear opti- representative thickness distribution data (10 layers mization is used to obtain a distribution of film thick- with alternating refractive indexes 2.3 and 1.4). Fur- nesses that produces the required characteristics [1]. It thermore, we clarified that training data sets gener- is therefore necessary to determine a set of initial values ated randomly lead to over-learning. It is necessary for the thickness of each dielectric film for the iterative to generate training data sets from larger data sets so optimization process. that the histogram of reflectivity becomes a flat dis- However, determining these initial values requires an tribution. In the future, we plan to apply this knowl- expert’s know-how. If optimization were performed from edge to design dielectric mirrors using neural network inappropriate initial values (i.e., obtained without expert approaches such as generative adversarial networks, knowledge), we would obtain only a locally optimal so- which do not require the know-how of experts. lution, which is unlikely to meet requirements. This is a particularly serious problem when designing a dielectric mirror, which has severe requirements. For example, Keywords: recurrent neural network, dielectric mirror, a chirped mirror, which is necessary to realize an ultra- optical design short laser pulse, requires both very high reflectivity and very low group delay dispersion over a wide range of target wavelengths [2]. It is impossible to design a dielectric mirror that meets the requirements for an exa watt-class

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© Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/). RNN for Predicting Dielectric Mirror Reﬂectivity

laser [3] by traditional design methods, and a new design from mirrors in this study. The reflectivity R of an in- process is necessary. put ray with wavelength λ is calculated in the following Machine learning is currently being applied in many paragraphs [6]. The direction of incidence of the ray is types of analysis to replace the know-how of experts. We assumed to be perpendicular to the mirror’s surface. believe that machine learning can also be used for design- The optical phase shift δi is calculated from ing dielectric mirrors; however, we are not aware of re- πd n δ = 2 i i , search to date in this area. It is not clear what kind of i λ ...... (1) neural network (NN) would be effective for this application. where the thickness and refractive index of each dielectric In this paper, as the first step in studying adapting film are denoted by di and ni, respectively. The index i is machine learning to dielectric mirror design, we com- used to number the film layers, counting from the base pared traditional fully connected simple neural networks plate. (FCNNs) and recurrent neural networks (RNNs) [4] for Assuming the base plate has a refractive index n,we this situation. The fully connected neural network does can get a vector of complex numbers α and β from not consider ordering in the input data, while the recur- Eq. (2), which will describe the characteristics of the di- rent neural network does. Because the ordering of the electric mirror. In this equation, Π denotes an infinite thin films is very important in the dielectric mirror (as de- product of matrices and j is the imaginary unit. ⎛ ⎛ ⎞⎞ scribed in Section 2), we chose the recurrent neural net- sin(δi) α (δ ) j work as suitable. = ⎝ ⎝ cos i n ⎠⎠ · 1 . β ∏ n (2) Therefore, we evaluated our RNN results in compari- i jnsin(δi) cos(δi) son to FCNN. Although curve fitting in itself is not our ob- jective, our models are evaluated using the mean squared The reflectivity R(λ) at a specific wavelength λ is then error of average reflectivity predicted by NN from the given by Eq. (3), using α and β. The refractive index of thickness distribution of films, compared to accurately the incident atmosphere is denoted by n0, and it will be calculated reflectivity. set to 1.0 in this study. When it becomes clear what kind of neural network (n α − β)2 + (n α − β)2 is suitable for learning about dielectric mirror charac- R(λ)=Re 0 Im 0 . 2 2 .. (3) teristics, the design method is expected to be drastically Re(n0α + β) + Im(n0α + β) changed. Although Eq. (3) calculates the reflectivity at a single wavelength, it is necessary to consider a range of wavelengths for this mirror application. The average reflectiv- 2. Properties of a Dielectric Mirror Made of ity Rave for a target wavelength range λs to λe is then: Multiple Films λe R(λ)dλ A dielectric mirror is made of thin films of dielectric λs Rave = ...... (4) materials and a base plate; the typical dielectric mirror λe has two kinds of dielectric material with different refrac- dλ λs tive indexes, alternately laminated onto a base plate. Each R boundary between thin films reflects incident light and The average reflectivity ave is therefore a function of n the interference between these reflected rays is controlled the refractive index i of each material and the thickness d by changing the thickness of each film. For example, a of each film, i. highly reflective mirror is designed so that all the rays reflected from each boundary between films reinforce each other, while an anti-reflection coating is designed so that 3. Prepared Data for Training the reflected rays cancel each other owing to destructive interference. Furthermore, a band-pass filter can be real- In this study, we developed a neural network system R ized by balancing both reinforcing and canceling of am- that predicts the average reflectivity ave from a vector d plitudes in the target wavelength range, and a chirped mir- that is dependent on the thickness i of each film layer ror can be designed for the characteristics of both reflec- and assumes only two refractive indexes, alternating be- tivity and group delay dispersion. tween layers. We prepared training and testing data for a A dielectric mirror can have a higher damage threshold dielectric mirror to evaluate the system’s learning ability. than a metallic mirror because thin films of stable mate- The target system has 10 layers of dielectric thin film rials such as oxides and fluorides have a lower absorp- of low and high refractive indexes, alternately stacked on tion ratio and a higher damage threshold than metals [5]. a glass substrate. For simplicity, the refractive indexes for Dielectric mirrors are therefore especially important for the films are set to 2.3 and 1.4, and for the base plate, 1.45. controlling high power density light from sources such as The target wavelength range is 800–1300 nm to consider lasers. exawatt-class lasers. Although we often think in terms of transmission We prepared 1000 data sets with the random thickness through an optical element, we consider only reflection of each of the 10 layers. The average thickness is 147 nm

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Fig. 1. Comparison of exact reflectivity distribution (aver- Fig. 2. Comparison between reflectivity predicted by the age values) for the original randomly generated data and the fully connected neural network and the exact reflectivity stratified data. Each group contains 1000 data items. (Different numbers of hidden layers are indicated).

and its standard deviation is 87 nm. For each combination, Python 3.6.3 using TensorFlow 1.5.0 [7] as the backend the exact average reflectivity was calculated by Eq. (4). library and Keras 2.1.3 [8] as the frontend library. The However, the simple randomly generated data has a bi- workstation for our calculation uses the OS Ubuntu 16.04, ased distribution of the reflectivity, as shown by white bins the CPU Intel Xeon 3.5 GHz, and the GPU NVIDIA in Fig. 1, which is a histogram for these data sets. The Tesra K40. original data sets have a peak reflectivity around 0.6 and The activation for the neural network used the ReLU the number of data of lower and higher reflectivity are in- function [9], the batch size was 100, and the epoch (iter- sufficient. ation) size was 4000. The optimizer used was Adam [10] Therefore, we generated 10,000 data sets randomly and and the optimization score function was the mean squared sampled 1000 data sets in a stratified manner from these error (MSE). so that the histogram becomes a flat distribution. The histogram of the stratified data sets is shown by the black 4.1. Fully Connected Neural Network with Increas- bins in Fig. 1. Although there are an insufficient number ing Hidden Layers of samples with reflectivity in the [0, 0.1) and (0.9, 1] in- tervals, they should be designed by nonlinear optimization At first, we used a simple neural network in which each as discussed in Section 1 because they are the important neuron is fully combined with each neuron in the previ- cases of the anti-reflection window and the high reflection ous and next layer; these act as hidden layers. We used mirror, respectively. To increase their number, we would different numbers of hidden layers: 5, 10, 20, 30, 40, and need to perform nonlinear optimization, but these samples 50. The output dimension of each hidden layer is set to are subject to bias, depending on the optimization method. 10 (the same size as the input vector) and the last output Therefore, the bulk of the stratified data sets were gener- is the scalar: average reflectivity. We used the Dense class ated randomly for this study. We used 80% of them as in the Keras library for its implementation. training data sets and 20% of them for examination. The relationship between the actual average reflectivity of the test data and the predicted average reflectivity (the learning result) is shown in Fig. 2. Even by increasing 4. Results About Machine Learning of the Re- the number of hidden layers, a good correlation is not obtained between the exact average reflectivity and the pre- lationship Between the Film Thickness Dis- dicted reflectivity. tribution and the Average Mirror Reflectiv- From these results, it is concluded that effective learn- ity ing is not achieved using a simple fully connected neural network, even by increasing the number of hidden layers. As described in Section 1, we compared the fully connected neural network and the recurrent neural network for their ability to learn the relationship between the re- 4.2. Applying Recurrent Neural Network sultant average reflectivity and the prescribed thickness To address the difficulties found with the simple fully distribution of dielectric films in a mirror. The thickness connected neural network described in Section 4.1, we ap- distribution of the 10 layers, as described in Section 3, is plied the recurrent neural network approach to this learn- treated as a one-dimensional input vector and the average ing relationship. reflectivity is the single scalar output. We built a model to learn how the reflectivity depends The machine learning program is implemented in on the order of thin films in a dielectric mirror. That

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Fig. 4. Mean squared error (MSE) dependence on num- Fig. 3. Comparison of machine learning predictions by the ber of hidden layers in the fully connected neural network recurrent network, for stratiﬁed data and original data. (FCNN).

means the first dielectric film is regarded as the first data point of the RNN input series, the second film is regarded as the second data point of the series, and so on. We used the SimpleRNN class of Keras for implementation of the learning program. In this subsection, we used both the randomly generated original data sets and the stratified data sets, previ- ously described in Section 3, as the training data sets. A comparison between the RNN-predicted average reflectivity using random data sets and stratified data sets is shown in Fig. 3. Unlike the case of a fully connected neural network, the actual average reflectivity is correctly predicted by RNN for both data sets. Furthermore, accurate predictions are obtained over a wider range by using Fig. 5. Maximum and minimum values of predicted re- the stratified data in comparison to the original random flectivity: Dependence on number of hidden layers in the data. FCNN. From these results, it is concluded that, unlike a simple fully connected neural network, a recurrent neural network that considers the order of the thin films is suitable is, increasing the number of hidden layers limits the pre- for learning about the performance of dielectric mirrors. dicted values to range of 0.36–0.74, which is much nar- rower range than its known exact range of 0.05–0.95. Therefore, the MSE of the predicted values does not de- 5. Discussion crease to a low enough level for all cases in this study; the fully connected neural network is therefore not suitable 5.1. Inability of the Fully Connected Neural Net- for learning the relationship between the average reflec- work to Predict Reflectivity tivity and the distribution of layer thickness in a dielectric mirror. The dependence of the mean squared error on the number of hidden layers in a fully connected neural network is shown in Fig. 4. Despite an increase in the number of 5.2. Advantage of RNN for Dielectric Mirror hidden layers, the MSE does not decrease significantly. A comparison between the MSE of the predicted value Figure 5 shows the maximum and minimum predicted obtained by a simple fully connected NN of 10 hidden value of reflectivity for different numbers of hidden lay- layers and that of a recurrent NN, using original data sets ers. When the number of layers is small, the predicted and stratified data sets for training, is shown in Fig. 6. average reflectivity is greater than 1 in some cases, even Although the simple FCNN has an MSE on the order though physically it must lie between 0 and 1. In other of 10−2, the RNN realizes much better predictions with words, unrealistic values are being predicted when the MSEs on the order of 10−3 and 10−4 with original and number of hidden layers used is small. By contrast, when stratified data sets, respectively. the number of hidden layers is large, the minimum pre- Reflection from a dielectric mirror is governed by in- dicted value increases and the maximum decreases. That terference between the multiple reflected rays from the

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Fig. 7. Error in recurrent neural network (RNN) predictions Fig. 6. Comparison of MSE between fully connected neural versus exact reﬂectivity: Comparison between original ran- network of 10 hidden layers and recurrent network. dom data and stratiﬁed data.

over-learned. By contrast, the stratified data have enough interfaces between thin films, as described in Section 1. information to accurately learn about the exact reflectivity Specifically, the reflection consists of interference be- of the dielectric mirror because their range of average re- tween (i) the reflected ray from the surface, (ii) the reflec- flectivity is larger than that of the original data. We must tion from each boundary between films of the ray trans- therefore consider both randomness and bias in the train- mitted through the front film, and (iii) the reflected ray ing data for learning with dielectric mirrors. from the base plate. Therefore, the properties of the reflected ray change if the order of the individual thin films is changed. In summary, the reflection from a dielectric 6. Conclusions and Future Works mirror depends on the specific sequence of thin film thicknesses in it. A study on neural network learning about dielectric As a result of the detailed mechanism leading to re- mirror reflectivity and its relation to the multiple-layer flection by a dielectric mirror, the simple fully connected structure was performed. Fully connected neural net- neural network cannot learn the properties of the dielec- works and recurrent neural networks were compared. tric mirror; it does not consider the order of the thin films. Each neural network learned the relationship between the This must be considered for an NN to effectively learn a reflectance and the distribution of dielectric layer thick- dielectric mirror’s properties. nesses, where the reflectance was averaged over a speci- In contrast to the FCNN, the recurrent NN is typically fied wavelength range. The training set used exact calcu- applied to learn about series data, particularly time depen- lations of the reflectivity for a distribution of layer thick- dent data, in which the order of input data is important. nesses. From this study, the following conclusions were RNNs have many achievements in this field, for example, reached: machine translation systems [11] and automatic speech recognition [12]. • The usual fully connected neural network cannot As described in Section 2 and especially as expressed learn to predict the average reflectivity of a dielec- in Eq. (2) using an infinite product of matrices, the re- tric mirror, even if the number of hidden layers is flectance of a dielectric mirror depends not on a time se- increased to 50. ries but on the physical sequence of dielectric films. Be- cause of this match with the device properties, the RNN • By contrast, a recurrent neural network can predict successfully learned to predict the average reflectivity of the average reflectivity of a dielectric mirror with a a dielectric mirror. MSE on the order of 10−3–10−4 using a dataset with Furthermore, Fig. 7 compares the RNN prediction error only 1000 items. for stratified input data and the original randomly generated data. The stratified data includes more cases of low • The RNN, which considers the order of the thin films or high reflectivity and fewer with a mid-range reflectivity in a dielectric mirror, can learn and predict its aver- (Fig. 1). However, the error of the predicted result using age reflectivity because the reflection from a dielec- the original random data is larger than that of the stratified tric mirror is composed of the interference between data for almost all ranges of exact reflectivity. This dis- the individual reflected rays from the sequence of crepancy remains true even in the reflectivity range 0.5– films. 0.7 where there is vastly more original than stratified data. This means that the original random data has a bias up- • The randomly generated training dataset based on ward in the average reflectivity (Fig. 1) and this bias is layer thicknesses in the dielectric mirror shows a bias

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in the wavelength-averaged exact reflectivity. There- fore, a dataset with a wide range of exact reflectivity Name: is necessary to realize accurate prediction by RNN Tomomasa Ohkubo by avoiding over-learning. Affiliation: Associate Professor, Tokyo University of Tech- Although a simple RNN was used in this study, RNN- nology variations such as LSTM (long short-term memory) [13] and GRU (gated recurrent units) [14] are being considered for future studies. We also plan to investigate the design of dielectric mirrors using other neural network approaches Address: such as generative adversarial networks (GANs), which 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan do not require the know-how of experts. Brief Biographical History: 2006 D.Sc., Tokyo Institute of Technology 2006-2007 Research Assistant Professor, Integrated Research Institute, Acknowledgements Tokyo Institute of Technology 2007-2014 Assistant Professor, Department of Mechanical Engineering This study is partially supported by Artificial Intelligence Re- and Sciences, Tokyo Institute of Technology search Group in Tokyo University of Technology. 2014-2015 Senior Assistant Professor, School of Media Science, Tokyo University of Technology 2015-2018 Senior Assistant Professor, Department of Mechanical Engineering, Tokyo University of Technology References: 2018- Associate Professor, Department of Mechanical Engineering, Tokyo [1] P. Baumeister, “Design of Multilayer Filters by Successive Approx- University of Technology imations,” J. of the Optical Society of America, Vol.48, Issue 12, pp. 955-958, 1958. Main Works: • [2] R. Szip˝ocs and A. K˝oházi-Kis, “Theory and design of chirped di- “Three-dimensional numerical simulation during laser processing of electric laser mirrors,” Applied Physics B, Vol.65, Issue 2, pp. 115- CFRP,” Applied Surface Science, Vol.417, pp. 104-107, 2017. 135, 1997. • “Solar pumped 80 W laser irradiated by a Fresnel lens,” Optics Letters, [3] Y. Fujimoto et al., “Development on ultra-broadband high intense Vol.34, No.2, pp. 175-177, 2009. laser propagation optics for exa-watt laser,” ICUIL, 2012. Membership in Academic Societies: [4] Y. Gal and Z. Ghahramani, “A theoretically grounded application of • The Laser Society of Japan dropout in recurrent neural networks,” Proc. of the 30th Int. Conf. • Japan Laser Processing Society (JLPS) on Neural Information Processing Systems (NIPS’16), pp. 1027- • The Japan Society of Applied Physics (JSAP) 1035, 2016. • The Japan Society of Mechanical Engineers (JSME) [5] S. Holmes and P. Kraatz, “Investigation of Pulsed CO Laser Dam- • The Optical Society (OSA) age in Coated Metal Mirrors and Dielectric-Coated Windows,” A. J. Glass and A. H. Guenter (Eds.), “Laser Induced Damage in Optical Materials: 1973,” pp. 138-150, U.S. Department of Commerce and National Bureau of Standards, 1973. [6] S. D. Smith, “Design of Multilayer Filters by Considering Two Ef- fective Interfaces,” J. of the Optical Society of America, Vol.48, Issue 1, pp. 43-50, 1958. Name: [7] M. Abadi et al., “TensorFlow: A system for large-scale machine Ei-ichi Matsunaga learning,” TensorFlow White Papers, 2015. [8] F. Chollet et al., “Keras,” 2015, http://keras.io [accessed February Affiliation: 18, 2019] Part-Time Lecturer, Tokyo University of Tech- [9] R. H. R. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, nology and H. S. Seung, “Digital selection and analogue amplification co- exist in a cortex-inspired silicon circuit,” Nature, Vol.405, pp. 947- 951, 2000. [10] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Opti- mization,” Proc. of the 3rd Int. Conf. for Learning Representations, 2015. Address: [11] Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan M. Krikun, Y. Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. Brief Biographical History: Johnson, X. Liu, Ł. Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, 2001 D.Sc., Tokyo Institute of Technology K. Stevens, G. Kurian, N. Patil, W. Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, O. Vinyals, G. Corrado, M. Hughes, and J. 2001-2005 Part-Time (crest) Researcher, The Earth Simulator Center Dean, “Google’s Neural Machine Translation System: Bridging the 2005-2012 Special Researcher, Tokyo Institute of Technology Gap between Human and Machine Translation,” arXiv:1609.08144, 2015- Part-Time Lecturer, Tokyo University of Technology 2016. Main Works: [12] A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with • “Using the Characteristic Equation to Estimate the Initial Values for deep recurrent neural networks,” Proc. of the 2013 IEEE Int. Conf. Numerical Forecasts,” J. Adv. Comput, Intell. Intell. Inform., Vol.20, on Acoustics, Speech and Signal Processing, pp. 6645-6649, 2013. No.7, pp. 1147-1151, 2016. [13] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” • “Baroclinic vortex generation by meteor and long term luminescence of Neural Computation, Vol.9, Issue 8, pp. 1735-1780, 1997. meteor duration train,” Proc. of the 14th Symp. on Computational Fluid [14] K. Cho, B. v. Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, Dynamics, Article No.A06-4, 2000. H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation,” arXiv: 1406.1078, 2014.

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Name: Name: Junji Kawanaka Shinji Motokoshi

Afﬁliation: Afﬁliation: Professor, Institute of Laser Engineering, Osaka Chief Researcher, Institute for Laser Technology University

Address: Address: 2-6 Yamadaoka, Suita, Osaka 565-0871, Japan 1-8-4 Utsubo-honmachi, Nishi-ku, Osaka 550-0004, Japan Brief Biographical History: Brief Biographical History: 1993 D.Sc., The University of Electro-Communications 1994 D.Eng., Osaka University 1994-1999 Assistant Professor, Faculty of Engineering, University of 1994- Researcher, Institute for Laser Technology Miyazaki 2006-2012 Specially Appointed Associate Professor, Tokyo Institute of 1999-2001 Researcher, Japan Atomic Energy Agency Technology 1999-2001 Assistant Principal Researcher, Japan Atomic Energy Agency Main Works: 2004-2018 Associate Professor, Institute of Laser Engineering, Osaka • Research and development of optical devices for high power lasers. University • Research of laser induced damages for optical devices. 2018- Professor, Institute of Laser Engineering, Osaka University Membership in Academic Societies: Main Works: • The Japan Society of Applied Physics (JSAP) • “Arbitrarily distorted 2-dimensional pulse-front measurement and • The Laser Society of Japan reliability analysis,” Optics Express, Vol.27, No.9, pp. 13292-13306, 2019. • “Room-temperature bonding with post-heat treatment for composite Yb:YAG ceramic lasers,” Optical Materials, Vol.91, pp. 344-348, 2019. Membership in Academic Societies: • The Optical Society (OSA) • The Laser Society of Japan Name: • The Japan Society of Applied Physics (JSAP) Kunio Yoshida

Afﬁliation: Specially Appointed Researcher, Institute of Laser Engineering, Osaka University Name: Takahisa Jitsuno

Afﬁliation: Address: Specially Appointed Professor, Institute of Laser 2-6 Yamadaoka, Suita, Osaka 565-0871, Japan Engineering, Osaka University Brief Biographical History: 1981 Doctor of Engineering, Osaka University 1993- Associate Professor, Institute of Laser Engineering, Osaka University 1994- Associate Professor, Osaka Institute of Technology Address: 1996- Professor, Osaka Institute of Technology 2-6 Yamadaoka, Suita, Osaka 565-0871, Japan 2005- Specially Appointed Professor, Institute of Innovative Research, Brief Biographical History: Tokyo Institute of Technology 1981 Ph.D., Konan University 2017- Specially Appointed Researcher, Institute of Laser Engineering, 1982-1983 Researcher, Promotion Center for Laser Technology Osaka University 1983-1987 Research Associate, Osaka University Membership in Academic Societies: 1987-2008 Associate Professor, Osaka University • The Laser Society of Japan 2008-2009 Professor, Osaka University • The Japan Society of Applied Physics (JSAP) 2009- Specially Appointed Professor, Institute of Laser Engineering, Osaka University Main Works: • “Split-aperture laser pulse compressor design tolerant to alignment and line-density differences,” Optics Letters, Vol.33, Issue 16, pp. 1902-1904, 2008. • “Laser ablative shaping of plastic optical components for phase control,” Applied Optics, Vol.38, Issue 15, pp. 3338-3342, 1999. Membership in Academic Societies: • The Optical Society of America • The Laser Society of Japan • The International Society for Optical Engineering

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