Seolhye Park

Wedge-shaped bowing during high-aspect-ratio Si trench etching under tailored voltage waveform (TVW)-driven dual-frequency capacitively coupled plasma conditions is analyzed from a plasma physics perspective. TVW operation modifies RF sheath dynamics, producing a bi-modal ion energy distribution with an enhanced high-energy component. When reflected from tapered SiO2 mask sidewalls, these ions induce localized energy-flux focusing near the upper trench region, resulting in sharp wedge-shaped profile deformation. Combining in situ diagnostics with feature-scale transport modeling, a plasma-information index, PIBow, is introduced by normalizing the ratio of reflected high-energy ion energy flux to transported O radical flux to a characteristic profile-transition reference scale. This dimensionless index quantifies the competition between ion-driven etching and radical-mediated passivation, and wedge-forming and non-wedge-forming conditions are systematically organized around the normalized reference state PIBow≈1 for the analyzed system. Furthermore, a plasma information–based virtual metrology framework then transforms optical emission spectroscopy and voltage-current sensor measurements into physics-informed plasma variables for wedge geometry prediction. The model reproduces the measured wedge length with a high coefficient of determination (adj. R2 = 0.981) and yields an interpretable process-control window governed by the balance between energetic-ion delivery and O-radical-mediated passivation. An angle-dependent etch rate model is also proposed to describe wedge angle formation, reproducing the measured dependence on the operating conditions. Wedge-shaped bowing thus represents a distinct regime of profile evolution governed by coupled ion dynamics and transport-limited radical supply under TVW conditions, and the methodology presented here offers a scalable framework for understanding, predicting, and engineering plasma–surface interactions in plasma-assisted manufacturing.

C2C (chamber-to-chamber) matching of the plasma processing reactors, called tool-to-tool matching, is one of the most important procedures to perform mass production in the organic light emitting diode display or semiconductor manufacturing facilities. Plasma processes, which adopt the RF power, are governed by the generation rate of reactive radicals and ions and their transportation to the reacting surfaces. Therefore, the characteristics of plasma heating, which determine the energy gain and relaxation of the electrons, are important information for performing C2C matching in the electron impact collisional inelastic processes dominated plasmas. In this study, we introduced the plasma heating characterizing PI (plasma information) index by using the limited sensor data accessible in the mass-producing fab with the help of a simple machine learning methodology to extract the information about the plasma heating properties. This index is applied to the C2C matching of the large-area capacitively coupled RF discharge-based PECVD (plasma-enhanced chemical vapor deposition) reactors successfully. In addition to the PI parameterization of the plasma heating characteristics in the large-area inductively coupled discharge applied plasma etcher introduced in the previous study, we could introduce the plasma heating characterized PI index for the PECVD reactors. From this, we could validate its utility to the artificial intelligence modeling, such as the plasma information-based virtual metrology and plasma information-based advanced process control, to optimize the productivity of the mass-producing factory.

Meter-scale of the large area inductively coupled plasma etchers with the capacitive power coupling are widely applied for the mass production of OLED (organic light emitting diode) display panels. Because of the large area-to-volume ratio of the etcher, the balance between the power loss and absorption is easily located in the capacitive coupling mode rather than the ideal inductively coupled mode. Therefore, the process results are sensitively governed by the power absorption and plasma heating properties of the reactors. We have introduced a new PI (plasma information) parameter, the ratio of the stochastic heating to Ohmic heating of the plasmas, which is monitorable by using the optical emission spectroscopy data of the processing etchers. With the help of this plasma heating characteristic index, we could optimize the process recipes with the detailed control of the etched hole sidewall passivation and related species generation rate in the plasmas; thus, chamber-to-chamber matching in the huge mass production fab with the higher efficiency was possible. It was demonstrated that the introduced PI index with plasma heating mechanism characterization could be applicable to the VM (virtual metrology) modeling as one of the good information supplying core variables. This PI index has shown a very high correlation with the plasma sheath and ion flux governing phenomena for a large number of mass-produced OLED display glasses. From these results, the introduced plasma heating mechanism-based PI index is expected to be utilized as a good reference index for their performance analysis or PI-VM modelings.

The production efficiencies of organic light emitting diode (OLED) displays and semiconductor manufacturing have been dramatically improving with the help of plasma physics and engineering technology by utilizing a process monitoring methodology based on physical domain knowledge. This domain knowledge consists of plasma-heating and sheath physics, plasma chemistry, and plasma-material surface reaction kinetics. They were applied to the plasma information based virtual metrology (PI-VM) algorithm with the plasma diagnostics and noticeably enhanced process prediction performance by parameterizing plasma information (PI) in various processes of OLED display and semiconductor manufacturing fabs. PI-VM has shown superior process prediction accuracy, which can trace the states of processing plasmas as an application of data-driven plasma science compared to the classical statistics and machine learning-based virtual metrologies; thus, various plasma processes have been managed and controlled with the help of the PI-VM models. More than this, we have adopted the PI-VM model to optimize the patterning architecture and plasma processes simultaneously. The best combination of the etching pattern structure and plasma condition was adjustable based on the detailed understanding of the angular distribution of sputtered atoms from the etching target surface and their interaction with the plasma sheath based on the PI-VM modeling for etching profile failure prediction.

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.

The effects of ion collisions on plasma–sheath formation are investigated experimentally for a low-density and low-pressure discharge. The space potential and ion velocity distribution measurements at high spatial resolution show that the ion collision properties observed in the presheath are maintained in the plasma–sheath transition region. The potential drop in the transition region indicates the existence of ionization as an effect of ion collisions in the transition region owing to the non-negligible density of the electrons penetrating the sheath. Based on comparisons between the space potential measurements and Riemann’s presheath–sheath transition solution, the ion collision length λ _i was determined as a key parameter in the presheath and transition region. And it represents that the thermal properties of ions and neutral gases affects space potential by the charge exchange and ionization collisions. The existence of the ion collision effect in the transition region suggests possible influence on the incident conditions of ions and electrons near the sheath edge. Consequently, the energy distributions of ions and electrons incident on the material surface facing the sheath are sensitive to the collisionality and operating conditions.

The high-value process strategy in the organic light emitting diode display manufacturing requires strict etching profile control to obtain the normal performance of the display pixels because of the correlation between the etched structure of the processed pattern and the current–voltage characteristics of the pixels. The critical dimension, etch depth, and sidewall angle area are important management points of the etching profile. Because of two-dimensional continuous characteristics of the display as the numerous arrays of the thin film transistors, the etching profiles should be controlled in the analog way rather than only the fact of spec-in or -out. Therefore, the sheath properties which govern the ion flux-driven etching effect and the polymer passivation characteristics of the sidewall of the etching hole are needed to be tailored spatially even in the micro-range. The embossing structured electrode surface applied for the large area display manufacturing dry etching process causes micro-scale non-uniformity of the sheath and passivation properties, and introduces the process faults. To control this micro-range non-uniformity of the etching profiles, a PI-VM (Plasma Information based Virtual Metrology) model was developed by applying the PI parameters. PI parameters include the information on the sheath potential for electronegative plasmas and the heat transfer properties in the chamber. PI parameterization utilized a big data pool of the optical emission spectroscopy and equipment engineering system data accumulated during the mass production. The developed PI-VM model could predict the fault with 80% accuracy, and analysis of the highly contributing PI parameters to the events could give the way to control the manufacturing.

In the semiconductor etch process, as the critical dimension (CD) decreases and the difficulty of the process control increases, in-situ and real-time etch profile monitoring becomes important. It leads to the development of virtual metrology (VM) technology, one of the measurement and inspection (MI) technology that predicts the etch profile during the process. Recently, VM to predict the etch depth using plasma information (PI) variables and the etch process data based on the statistical regression method had been developed and demonstrated high performance. In this study, VM using PI variables, named PI-VM, was extended to monitor the etch profile and investigated the role of PI variables and features of PI-VM. PI variables are obtained through analysis on optical emission spectrum data. The features in PI-VM are investigated in terms of plasma physics and etch kinetics. The PI-VM is developed to monitor the etch depth, bowing CD, etch depth times bowing CD (rectangular model), and etch area model (non-rectangular model). PI-VM for etch depth and bowing CD showed high prediction accuracy of R-square value (R²) 0.8 or higher. The rectangular and non-rectangular etch area model PI-VM showed prediction accuracy R² of 0.78 and 0.49, respectively. The first trial of virtual metrology to monitor the etch profile will contribute to the development of the etch profile control technology.