Bulletin of the American Physical Society
71st Annual Gaseous Electronics Conference
Volume 63, Number 10
Monday–Friday, November 5–9, 2018; Portland, Oregon
Session NR2: Plasma Etching |
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Chair: Costel Biloiu, Applied Materials Room: Oregon Convention Center A105 |
Thursday, November 8, 2018 8:00AM - 8:15AM |
NR2.00001: Optimization of Etching Recipe Using Deep Neural Network Yutaka Okuyama, Takeshi Ohmori, Hyakka Nakada, Masaru Kurihara Increasing the number of control parameters in etchers has achieved nanoscale semiconductor processing. However, optimizing etching recipes with such a large number of parameters has made it difficult to obtain a desirable profile. In this work, a deep neural network (DNN) [1] has been applied to predict etching recipes for vertical trench etching. Training data consisted of seven input parameters in an etching recipe and nine output parameters representing a trench profile, and 82 samples which had no vertical trench were prepared as an initial dataset. Several target profiles were set to exceed the best profile in the dataset and gradually approach a deep vertical trench. The predicted recipes for these targets were verified by etching experiments, and these results were used to update the training dataset. An optimization cycle consisting of training, prediction, verification, updating the dataset, and updating targets to a depth exceeding 1000 nm was repeated. The vertical trench was realized in the fourth cycle with 102 samples. Our recipe prediction method was found to be very effective in searching for the optimum recipe and applicable in obtaining desirable results in semiconductor manufacturing tools. [1] I. Goodfellow, “Deep Learning,” MIT Press, 2016. [Preview Abstract] |
Thursday, November 8, 2018 8:15AM - 8:30AM |
NR2.00002: Optimization of Etching Recipe Using Reinforcement Learning Hyakka Nakada, Tatehito Usui, Takeshi Ohmori As the semiconductor devices have changed from 2D to miniaturized 3D structures, the process development period has been increased. The long development period has caused the device cost to increase. In etching processes, the number of steps of a recipe, which is a set of etcher parameters for plasma conditions, has increased beyond 10. Rapid optimization of multi-step recipes to obtain target profiles is required to reduce the development period. In our previous study, a recipe-exploration method utilizing machine learning was developed to optimize single-step recipes [1]. In this method, supervised learning was utilized to learn the non-linear relationship between etching profiles and recipes. However, they are considered to require a large number of learning data because the number of recipe parameters increases as the number of steps increases. In this study, we developed a data-efficient method to optimize multi-step recipes with reinforcement learning, which is specialized for optimizing a plurality of operations [2]. We succeeded in real-time control of multi-step recipes during the plasma process to etch the target profile. [1] H. Nakada, et al., GEC2017, [2] D. Silver, et al., Nature, vol. 529, pp. 484-489 (2016) [Preview Abstract] |
Thursday, November 8, 2018 8:30AM - 8:45AM |
NR2.00003: Optimizing Plasma Etching of High Aspect Ratio Oxide-Nitride-Oxide Stacks Shuo Huang, Chad Huard, Sang Ki Nam, Seungbo Shim, Wonyup Ko, Mark J. Kushner BiCS (bit cost scalable) fabrication of semiconductor memory is now addressed by 3-dimensional structures. One of their critical fabrication challenges is the plasma etching of high aspect ratio (HAR) vias with aspect ratios (AR) up to 100 through hundreds of alternately deposited silicon oxide and silicon nitride layers. Such processing typically requires high energy (several keV) ions and carefully controlled neutral-to-ion flux ratios. With nm scale critical dimensions (CD), when high energy ions impact the surface, the etch yield (removed atoms per incident ion) can exceed unity, resulting in both higher etch rate and aggravated roughness of edges, particularly at the mask resulting in contact-edge-roughness (CER). In this paper, we report on a computational investigation of the plasma etching of oxide-nitride-oxide (ONO) stacks using the 3-dimensional Monte Carlo Feature Profile Model with a newly developed polymer mediated fluorocarbon etching mechanism for oxide and nitride. Energy and angularly resolved fluxes of ions and neutral radicals to the surface are provided by the Hybrid Plasma Equipment Model for multi-frequency capacitively coupled plasmas sustained in Ar/O$_{\mathrm{2}}$/C$_{\mathrm{4}}$F$_{\mathrm{8}}$ mixtures. Fluxes to the etch front as a function of AR for the etching of ONO stacks, and scaling laws for maintaining CD while addressing feature distortion (e.g., twisting, CER) will be discussed [Preview Abstract] |
Thursday, November 8, 2018 8:45AM - 9:00AM |
NR2.00004: Etching of GeSe in an inductively coupled SF6 plasma Meyer Thibaut, Aurelie Girard, Christophe Cardinaud, Emeline Baudet, Petr Nemec, Virginie Nazabal This study is focused on Ge-Se glasses that are the fundamental part of more complex ternary chalcogenides as Ge-Sb-Se, Ge-As-Se. An Inductively Coupled Plasma (ICP) reactor allows to control independently the plasma density and the substrate bias. Plasma diagnostics such as optical emission spectroscopy, mass spectrometry and electrostatics probes are performed during the GeSe etching. These measurements provide some understanding about the volatile products, which are SeF$_4$, SeF$_6$, GeF$_2$, and GeF$_4$, by monitoring neutrals and ions intensities. The interaction between volatile products and SF$_6$ plasma are directly explained by the variation of plasma parameters measured by the Langmuir probe. Direct parameters such as the source power (700 and 1000 W), the pressure (3 mTorr to 30 mTorr), the flow rate (10 to 40 sccm) and the bias (0 to -150 V) are investigated. Substrate temperature is maintained at 20 ºC. Indirect parameters such as the ionic density or the atomic fluorine concentration are investigated by mixing SF$_6$, respectively with Ar or O$_2$. In situ X-ray Photoelectron Spectroscopy gives information on composition variation or the oxide formation regarding the etching conditions. Some results will be compared with the etching of Ge-Sb-Se glasses. [Preview Abstract] |
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