bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Driving Tool, Digital Twin, and Machine Learning Algorithms for the Real Time Optimization of the ALD Growth of Multicomponent Thin Films Using in Situ Techniques
Authors: Yanguas-Gil Angel , Elam Jeffrey W.
Published: December 2024

The design of optimal sequences for multicomponent ALD processes can be a time-consuming procedure: transient effects can appear each time that one switches to a different precursor, making it hard to predict beforehand the desired composition. Consequently, researchers usually resort to trial runs to calibrate the film composition, and combination of precursors with well-behaved nucleation behaviors and fast transients are highly prized. While effective for processes involving two different precursors, this empirical approach makes it hard to extend ALD to explore more complex materials involving ternary or quaternary compounds.

In this work we explore an alternative approach that uses machine learning integrated with in-situ techniques to optimize the composition of multicomponent films. Our approach relies on digital twins to train, optimize, and benchmark algorithms that build the sequence of ALD cycles in real time from the feedback of in-situ data. These models allow us to generate a wide range of processes with different types of nucleation behaviors, providing the ideal testbed for the development of robust algorithms. We then transfer these algorithms into our experimental reactors for the exploration and optimization of processes involving two and three different types of oxide materials. Our results show that it is possible to drive the optimization of binary and ternary ALD processes using solely the growth per cycle of the individual processes as input, with the algorithm being robust across different nucleation behaviors. The main limitation of the proposed approach is that, for techniques showing a net change in the material, such as quartz crystal microbalance, it is not possible to consider systems that exhibit a substantial amount of etching, such the ZnO/Al2O3 process involving diethyl zinc and trimethyaluminum precursors. This would not be a limitation for techniques providing direct compositional information, such as X-ray fluorescence, or those focusing on materials properties, such as in-situ spectroscopic ellipsometry.

This research is based on the work supported by the Laboratory Directed Research and Development (LDRD) funding from the Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. DOE under Contract No. DE-AC02-06CH11357.
High Speed Spatial Peald of 3D Objects Using Single-Sided and Double-Sided Plasma Exposure
Authors: Dickey Eric
Published: December 2024

Spatial ALD offers a path to major increases in deposition speed without sacrificing the unique properties of conventional temporally processed ALD coatings, while also enabling unique processes not suitable for temporal ALD. Most spatial ALD systems to date have been developed for coating flat surfaces such as thin wafers or glass panels, and rely on narrow channels and passageways, combined with directed gas flows and differential pumping to keep the ALD precursors and reactants separated from each other as the substrates are transported between the respective regions of the reactor. In some cases, these gaps must be maintained to within a few tens of microns to prevent vapor phase precursor/reactant interaction, and the resulting unwanted CVD growth. Thus, the use of this method for precursor separation is not suitable for relatively tall and irregularly shaped substrates such as optical lenses, or assembled electronics and components, such as stuffed circuit boards or electronic devices. In this work, a novel method of preventing precursor/reactant interaction it utilized to allow large, tall passageways for substrate movement between a confined DC oxygen plasma zone and a large open area metal precursor zone in a rotary spatial PEALD reactor. Using this configuration, uniform ALD SiO2 coatings are demonstrated on spherical substrates one centimeter in diameter using a single planar plasma electrode facing only one surface of the substrate, at speeds up to six angstroms per second. A second configuration for the plasma source, encompassing two opposing electrodes, is then used to demonstrate uniform deposition on spherical substrates two centimeters in diameter.
(Invited) Prospects of New Cleaning Technologies for Future Device Development
Authors: CHA JI Hoon
Published: December 2024

As devices shrink, new materials and new stacked structures are being introduced to improve performance and overcome structural limitations. For memory devices, the higher stacked structure, which was mainly used in Flash devices, is being extended to Dram devices. The structure for logic devices is transitioning from planar, Finfet and GAA to Forksheet and CFET.

In order to develop processes for a future device, the cleaning process has also become increasingly sophisticated and difficult. In particular, the requirements for fundamental cleaning techniques such as highly selective and narrow gap etching, small particle removal and extreme uniformity control are being pushed to the limit.

In this presentation, we will review trends in device roadmaps and suggest new cleaning technologies needed for future devices. New chemicals with inhibitors and dry cleaning processes using radical or etching gas are being evaluated for highly selective etching of materials with different Ge content, such as SiGe 5~50%. New concepts will be suggested for small particle removal without pattern damage using polymers and for selective deposition by Wet SAM (Self -Assembly Monolayer) which could have various applications. In terms of small particle removal, we use polymers that can be attached to small particles, like sticky materials, which remove them without any other residues. In this process, it is important to remove small particles without causing any other damages to the pattern. For SAM, it can chemically adsorb in the area where material does not want to be deposited and stays during subsequent deposition, resulting in a selective deposition. Any SAM after deposition should be removed by plasma treatment.

In addition, reclaimable chemicals have been expanding to promote environmental sustainability and cost efficiency. To achieve this, a real-time chemical analyzer that accurately and quickly monitors the chemical composition of elements is essential. The development of recyclable chemicals, as well as hardware equipment, is also needed.
メガヘルツ周波数範囲のクォーツトランスデューサー超音波洗浄機を使用したポリスチレンラテックス粒子の除去の評価
Authors: Seike Yoshiyuki , Takagi Rai , Hikita Tomomi , Honda Yuji , Taoka Noriyuki , Ichino Yusuke , Mori Tatsuo
Published: December 2024

This paper describes the newly developed quartz transducer ultrasonic cleaners (QTCs) operating at frequencies of 1 MHz to 3 MHz, designed to physically remove particles, including a few nano-sized particles, without causing damage to wiring patterns. The paper also discusses the potential of QTC by conducting experiments to remove submicron polystyrene latex (PSL) particles using QTC.

In recent years, with the refinement and increased integration of semiconductors, there is a growing need to remove nanometer-sized particles. Conventional spray-type ultrasonic cleaners have the disadvantage of easily disrupting wiring patterns due to fluctuating sound pressure impacting the substrate surface. the QTCs were developed with the consistent sound pressure fluctuation. The QTC transmits ultrasonic waves from an ultrasonic oscillator through an elliptical quartz column and then impacts the particles on the wafer surface through the pure water between the QTC and the wafer surface. Therefore, the variation of sound pressure on the wafer surface is small. We measured the sound pressure emitted by the QTCs and confirmed that its fluctuation is significantly smaller than that of conventional spray-type ultrasonic cleaners, which exhibit large and random fluctuations in sound pressure.

Subsequently, PLS particles ranging from 100 nm to 1 µm were coated onto the surface of silicon wafers with oxide film. These particles were then removed using QTCs operating at frequencies of 1, 2, and 3 MHz to elucidate the physical particle removal mechanism. The experimental results showed that the particle removal rates (PREs) tended to decrease as the particle size decreased. Regarding frequency dependence, the PREs increase with higher frequencies and also with higher amplitudes of the QTC. The removal of PSL particles in spray-type ultrasonic cleaning has been confirmed using drag force analysis. It has been observed that smaller PSL particles result in lower drag forces, leading to a decrease in the PRE. The same trend is observed in the present experiment. However, when the relationship between drag forces and PREs was confirmed using QTCs, the variations of PRE became significantly smaller as the frequency of QTCs increased. From this result, it was confirmed that the cleaning method developed this time with QTCs is easy to control with less turnover using a high frequency in the megahertz band.
(Invited) Germanium Layer Transfer for Advanced CMOS
Authors: Maeda Tatsuro
Published: December 2024

Ge has shown great potential for use in CMOS technology due to its higher electron and hole mobility compared to Si. In particular, Ge on insulator (GOI) is highly desirable for enhancing the performance and efficiency of CMOS. For the ultimate scaled high-performance and low-power dissipation CMOS, nanosheet or multi-stacked channels are crucial structures for improved gate control and immunity against short channel effects. Therefore, the layer transfer technology of Ge onto Si has attracted a lot of attention. To realize Ge stacked channel on current mainstream Si wafers, layer transfer technology utilizing direct bonding and selective etching was developed, showing great potential for ultimate device structures with evolutionary device architecture from fin to complimentary FET. Key challenges still remain about maintaining the material quality of transferred layers from the donor wafer to the fabricated structure and how to fabricate high-performance devices under low-thermal budget processes on Si. In this talk, we discuss the layer transfer technology for integrating Ge devices for advanced CMOS.

Figure 1
(Invited) Silicon Carbide Technology Scaling: Challenges and Opportunities across the Value Chain
Authors: Singh Navab , Chand Umesh , Kumar Shiv , Nguyen Xuan Sang Xuan , Bera Lakshmi Kanta , Chung Surasit , Gong Xiao , Yeo Yee Chia
Published: December 2024

Silicon Carbide (SiC) technology has emerged as a promising solution to address the growing demand for high-performance power electronics in various applications, ranging from automotive to renewable energy systems. As the industry continues to push the boundaries of SiC technology, scaling opportunities and challenges arise across the entire value chain, from material to device fabrication and new package architectures. In addition, it should be noted that scientific understanding, especially in the areas of defect behaviour in the SiC epitaxy layer, threshold voltage instability, interface trap density, and integrity of the gate insulator, would be increasingly important and necessary for the further development of SiC power devices. To address the economics, and mass produce ultra-high-power electronics devices with improved performance and reliability, we partnered with ecosystem players across the value chain from substrate to power module packaging and set up a 200mm SiC specialty open innovation process line.

This talk will provide an overview of the key challenges and opportunities associated with SiC technology. We will discuss the high grow rate (≥50µm/hr) SiC epitaxy process and share methodologies to achieve high-quality epitaxy layer with good doping control (≤10%), tight thickness uniformity (≤3%), low surface roughness (≤3Å) and low crystal defect densities (≤0.4 cm-2) on 200mm 4H-SiC substrate. Moving on to the metal oxide semiconductor (MOS) device interface engineering, we will discuss strategies for achieving low interface trap densities (~1011eV-1 cm-2) and improved threshold voltage instability. Beyond device fabrication, we will highlight the importance of the power module packaging technique in realizing the full potential of SiC technology. This includes the double-side liquid cooling system and the power dissipation capability with different designs and material options for the proposed 6-in-1 power module.

Overall, by addressing these challenges collaboratively, we can accelerate the adoption of SiC technology and unlock its full potential to drive power semiconductor technology and beyond.

Figure 1
(Invited) Epitaxial Growth and Development of Complimentary Field-Effect Transistors
Authors: Arora Himani , Tolle John
Published: December 2024

The semiconductor industry is currently at a pivotal juncture, with major chip fabrication facilities having exhausted FINFET technology and now transitioning to a new device architecture of gate all around (GAA). The next architecture in this progression (beyond 1nm), crucial for scaling down is Complementary Field-Effect Transistors (cFETs). In cFETs, nMOS and pMOS are stacked on top of each other, eliminating the gap and resulting in significant reduction of area and maximization of channel width and drive current.[1-3]

In this work, we present our research on epitaxial growth of cFET stacks and discuss our design strategies and the key evaluation metrics in accordance with the downstream processes. We delve deeper into the bottom-up growth of cFETs using Chemical Vapor Deposition (CVD) epitaxy and explore the impact of temperature, precursor gases, and Germanium content on the quality and strain engineering of cFETs. We exploit various characterization techniques to quantify defects, strain, uniformity, and composition of the epitaxial layers comprising the cFET stacks, followed by tuning of the process parameters accordingly to achieve a high-quality, fully-strained stack. Through this study, we aim to provide valuable insights into the growth and optimization of cFET structures, thereby facilitating their integration into future transistor nodes.

[1] P. Schuddincket al., 2022 IEEE Symp. on VLSI Techn. & Circuits, Digest of Technical Papers, p. 365.

[2] S. Subramanian et al., 2020 IEEE Symp. on VLSI Techn. & Circuits, Digest of Technical Papers, TH3.1.

[3] A. Vandooren et al., 2022 IEEE Symp. on VLSI Techn. & Circuits, Digest of Technical Papers, p. 330.
(Invited) Millimeter-Wave and THz Circuits and Sub-Systems in Si-Ge Bi-CMOS Technology
Authors: Zeinolabedinzadeh Saeed
Published: December 2024

SiGe BiCMOS technology offers high-speed, low-noise characteristics, coupled with a high breakdown voltage and favorable integration capability with high yield, making it ideal for developing high performance complex integrated circuits and systems in silicon. Recent advancements have resulted in devices with an fmax exceeding 500 GHz, enabling the creation of circuits operating at higher millimeter-wave (mmW) and sub-mmW frequencies. The trajectory of SiGe technology indicates its potential to extend operational speeds toward THz frequencies, possibly surpassing CMOS devices through scaling path. Moreover, SiGe technology demonstrates favorable performance under extreme environments, particularly exhibiting improved performance at low temperatures.

In parallel with progress in device technology, significant advancements in circuit technology have been achieved, leading to the realization of sophisticated integrated RF front-end circuits for various commercial applications such as 5G/6G communication, automotive radar, and THz imaging.

This presentation will showcase recent research conducted by the author’s team at Arizona State University (ASU), focusing on the design and development of integrated mmW and THz transceiver circuits and subsystems utilizing SiGe BiCMOS technologies. Our research incorporates device, circuits, and electromagnetic co-design practices, demonstrating close alignment between the designed and measured characteristics of the circuits at mmW and THz frequencies.

The presentation will feature implemented examples of transceiver front-end circuits and coherent transceiver arrays. These chip-scale circuits were specifically designed for centralized and distributed beamforming, incorporating high precision synchronization capabilities tailored for 5G mmW and ranging applications. Additionally, fully integrated THz circuits will be presented, demonstrating their effectiveness for chip-scale spectroscopy and THz imaging applications.
Deposition of SiC Thin Films By Using R. F. Power Ramps
Authors: Perez Ladron de Guevara Hector , Esparza Ramírez Kevin Manuel , Rosendo Andrés Enrique , Zarazua Macias Isaac , Medel Ruíz Carlos Israel
Published: December 2024

SiC is a broadly researched and utilized material in a wide variety of known applications like coatings, MOSFETs and other miscellaneous devices; Regardless of this, SiC technology still has potential growth in new material engineering and devices through use of modern growth techniques and methods. In this study, we attempted to deposit SiC films on Si and glass substrates by using low temperature and different R. F. power ramps in a Magnetron Sputtering.

The experiment presented successful growth of SiC thin films through a novel ramping method that allowed us to deposit a thin film at room temperature. The films were characterized by using AFM Microscopy, Spectroscopic Ellipsometry and Micro-Raman Spectroscopy. The valuable information obtained on surface morphology, band gap energy and crystal structure will fuel further endeavors to continually develop this material for future applications.

AFM measurements presented a periodic grain pattern akin to terraces with quadrilateral grains and a meaningfully different geometry when compared to traditional semiconductor surfaces. Ellipsometry data provided a band gap energy from 3.3 eV. Raman spectra revealed broad peaks pertaining to multiple SiC polytypes.
(Invited) Boron δ-Doping and Silicon Epitaxy on Chlorinated Silicon Surfaces
Authors: Butera Robert E.
Published: December 2024

The halogen-silicon system has been well-studied over the years due to the prevalent use of halogen-based plasmas for silicon device processing and for its role in fostering chemical reactions leading to the functionalization of silicon surfaces. Most recently, our work has demonstrated the effectiveness of a single monatomic layer of chlorine to resist the adsorption of chlorinated molecular dopant precursor gases and support the epitaxial growth of silicon from a molecular beam source. We have leveraged these findings to realize atomic-scale quantum devices in which we are able to confine dopant atoms to atomically-precise regions of a nominal single plane of silicon with sufficient density to support ohmic conduction across these structures at mK temperatures. Here, I will discuss our process of using BCl3 to obtain single layers of heavily boron-doped silicon with an extremely high active carrier concentration (> 1014 cm-2), as well as the role of the chlorinated silicon surface in supporting the growth of high quality, epitaxial silicon capping layers.
Pathways Toward High-Energy Li-Sulfur Batteries, Identified Via Multi-Reaction Chemical Modeling
Authors: DeCaluwe Steven C. , Korff Daniel M.
Published: December 2024

Lithium-sulfur (Li-S) batteries, which promise high gravimetric capacity and use Earth-abundant raw materials, could accelerate global energy decarbonization. Currently, Li-S performance is limited by non-conductive cathode phases (solid sulfur S8(s), lithium disulfide Li2S(s)), PS shuttling and precipitation at the anode, and sluggish Li+ transport. Mitigation often aims to control the reaction pathways or prevent PS shuttle, but a better understanding of the underlying phenomena is needed to guide development.

Here we present a 1D model of a Li-Sulfur battery with physically derived geometric parameters and thermodynamically consistent electrochemical kinetics. The approach enables straightforward comparison of proposed Li-S mechanisms and provides insights into the influence of polysulfide intermediates on battery discharge. Comparing predictions from multiple mechanisms demonstrates the need for both lithiated and non-lithiated polysulfide species and highlights the challenge of developing parameter estimates for complex electrochemical mechanisms. The model is also used to explore cathode design strategies. Discharge performance and polysulfide concentrations for electrolyte/sulfur ratios in the range 2–4 μL mg−1 identifies trade-offs that limit battery energy and power density and highlights the risk of polysulfide precipitation. New cathode and electrolyte approaches must limit polysulfide concentrations in the electrolyte, both to unlock better rate capabilities in Li-S technology and to prevent capacity fade due to polysulfide precipitation. Moreover, the tools developed and presented here present a pathway to flexibly incorporate atomistically-derived reduction-oxidation thermos-kinetics and polysulfide transport properties to unlock predictive models for design and control of high-performance Li-S batteries.
Graph Encoders for Redox Potentials and Solubility Predictions
Authors: Furth Nicholas , Imel Adam , Zawodzinski Thomas A.
Published: December 2024

Redox potential and solubility of organic redox-active molecules are important parameters for electrochemical devices such as aqueous and non-aqueous redox flow batteries (RFBs). The redox potential is a property of the organic molecules and is by the solvent used, while the solubility of the active species determines the volumetric capacity. When the solubility and redox potential values are multiplied, they provide insight into the energy density of the RFB, making them important properties in the feasibility and efficiency of RFBs. Other factors, such as supporting electrolytes, solvents, temperatures, and pH levels, can also affect the redox potential and solubility, making this a multi-dimensional problem. As a result, the simulation or prediction of performance characteristics for newly synthesized organic redox-active molecules is not straightforward, requiring each possible configuration of parameters, i.e., solvents,

electrolytes, and operating conditions, to be experimentally determined.

Current methods to simulate redox potential and solubility require advanced modeling techniques such as COSMO-RS, density functional theory, or molecular mechanics. Recently, machine learning (ML) models are being used to capture the intricate relationship between all parameters in complex systems such as problems that involve molecular search and property prediction. Graph neural networks (GNNs) and variational auto-encoders (VAEs) are powerful ML tools for rapidly and accurately predicting the characteristics of chemical systems. One limitation of typical GNNs is that the input can only be a single graph. We are investigating this shortcoming by utilizing multiple GNNs as part of VAE models that encode the graph inputs, which represent molecular structures, to latent space vectors. The different latent space vectors are then concatenated into a single vector, which overcomes the limitation of only permitting a single graph per input. Additionally, the different latent space vectors can be scale to account for concentrations. Our method allows the different encoder models for the redox-active material, support electrolytes, and solubility to be individually optimized while simultaneously providing a more precise optimization.

We will show how our approach allows GNNs to solve problems that consist of multiple molecular structures as inputs, such as redox potential and solubility. We then use this to predict the redox potential and solubility in various electrolyte systems to demonstrate the effectiveness of our approach. Additionally, we explore and analyze different classes of GNNs, such as graph convolutional network (GCN), crystal graph convolution neural network (CGCNN), and message passing neural network (MPNN), as the graph encoding model. Ultimately, we show that our approach provides fast and efficient predictions of complex systems and is easily transferable to other problems with multiple chemical components.
Enhanced Selectivity and Reduced Arde in Nanoscale Si Trench Etching: The Effects of Asynchronously Pulsed Plasmas, Bias Parameters, and Fluorocarbon Gas Additives
Authors: Ho Su Min , Kim Hee Ju , Kim Min Cheol , Yeom Geun Young
Published: December 2024

The continuous miniaturization of semiconductor devices made it increasingly difficult to achieve high aspect ratio (HAR) features with precise control over the profile due to low etch selectivity over mask and aspect ratio dependent etching (ARDE). In this study, we investigated the effects of pulsed plasma modes, bias pulsing parameters, and additive gases(CF4 and C4F8) on the etching characteristics of nanoscale silicon (Si) trench using inductively coupled plasmas (ICPs) with Cl2/Ar gas mixtures.

Compared to CW plasmas, synchronously pulsed plasmas exhibit improved Si etch profiles and reduced ARDE effects. Furthermore, asynchronously pulsed plasmas showed further decreased in the ARDE effect compared to synchronously pulsed plasmas. The additive gases changed the Si trench sidewall etch profiles by protecting Si trench sidewalls during etching using asynchronously pulsed plasmas. To understand the etch mechanism of pulsed plasmas, plasmas have been characterized with high voltage probes, time resolved optical emission spectroscopy (OES), a residual gas analyzer (RGA), and a retarding field energy analyzer (RFEA). Also, the XPS measurement has been performed to understand chemical reactions on the etched material surfaces.

Experimental results demonstrated that, by controlling bias pulsing parameters and utilizing additive gases, nanoscale Si trench etch characteristics can be more precisely managed. This technique showed a promise for advanced etching applications requiring HAR features, such as nano-through silicon vias (TSVs), shallow trench isolation (STI), etc., making it a valuable method for next-generation semiconductor device fabrication.
Uncovering Conversion Reaction Mechanism of FeF2 Cathode with Machine Learning Force Field
Authors: Pu Jiankun , Viswanathan Venkat
Published: December 2024

In the pursuit of higher energy densities crucial for electrifying the transportation sector, this study employs a machine learning (ML) force field to explore the conversion reaction mechanisms of Li-FeF2 battery cathodes. By leveraging ML, we accelerate simulation times significantly while maintaining the fidelity of traditional ab-initio methods. Our analysis focuses on the cation diffusion pathways and charge transfer processes during battery operation. We specifically investigate how the coherence of the LiF/Fe interface impacts the diffusion activation barriers of Fe. The ML-enhanced simulations uncover fundamental mechanisms governing cathode cycling behavior, previously unachievable with compute-intensive density functional theory. Additionally, we explore the intrinsic diffusion limitations of Fe at the LiF/Fe interface, providing insights crucial for optimizing cathode particle sizes to enhance battery performance. Our findings not only deepen the understanding of battery chemistry but also guide the design of next-generation batteries, underscoring the broad applicability of AI and ML in characterizing battery materials.
Deep Generative Model Based Screening of Electrochemically Active Organic Compounds for Redox Flow Batteries
Authors: Ortega Ochoa Raul , Bhowmik Arghya , Vegge Tejs
Published: December 2024

Electrochemically active organic molecules offer a promising path towards scalable solutions for sustainable energy storage, e.g., in redox flow batteries. Existing redox molecules, however, fail to combine viable redox properties with sufficient chemical and electrochemical stability. Given the multi-dimensionality and vastness of the chemical space of this set of compounds, discovering novel candidates with desired properties (sometimes referred to as inverse design) conforms a great challenge. In previous work (Materials Funnel 2.0), we demonstrate that Deep Generative Models for materials can be used together with Deep Learning surrogate models to efficiently screen for novel compounds with sought-after properties in vast chemical spaces.

In the present work we make use of such an updated funnel 2.0 approach to screen for electrochemically active organic compounds for the anode and cathode sides of a redox flow battery. We demonstrate the great utility of employing the Materials Funnel 2.0 approach to screen vast and multi-dimensional chemical spaces for novel electrochemical organic molecules with three criteria: catholyte/anolyte redox potential, water solubility, ease of synthesis. A subset of all the candidates found by our tool are then further studied to validate the approach.
Big Data for the Diagnosis and Prognosis of Deployed Energy Storage Systems
Authors: Yasir Fahim , Dubarry Matthieu
Published: December 2024

The diagnosis and prognosis of deployed battery energy storage systems is difficult because the battery might never experience controlled conditions through normal operation as both the day-to-day charge and discharge duty cycles might be sporadic. To circumvent this issue new methodologies must be developed, and we recently proposed one for photovoltaic connected batteries. The method is using real observed solar irradiance, modeled clear sky irradiance and synthetically generated battery data from a battery digital twin to diagnose the battery degradation. The approach was demonstrated to be effective for opportunistic diagnosis without the need for any maintenance cycles for nickel manganese cobalt oxide-based batteries for days with over 50% clear sky or with an average irradiance over 650 W/m2.

This work is validating the approach further for lithium iron phosphate-based batteries. We applied our data-driven diagnosis technique to synthetically generated PV connected LFP cells and tested more than 10,000 different degradations on 720 unique days. Overall, the results showcase that, similarly to what was observed for the NMC cell, diagnosis is possible for days above 50% clear sky or for high average irradiance.

The main difference was that LAMPE estimation was proven much more problematic for LFP Cells, especially for small LAMPEs. This, however, should not hamper deployability since the analysis of the impact of the degradation composition and extent indicated that the accuracy of the diagnosis is restored when LAMPE is prominent, and thus a potential safety hazard. This additional analysis showcases the benefits of using varied synthetic datasets to investigate the impact of all possible degradation on the cells.
Voltage Mining for (De)Lithiation-Stabilized Cathodes and a Machine-Learning Model for Li-Ion Cathode Voltage
Authors: Li Haoming Howard , Chen Qian , Ceder Gerbrand , Persson Kristin A.
Published: December 2024

Advances in Li-metal anodes have inspired interests in discovery for Li-free cathodes, most of which can be more formally defined as “delithiation-stabilized”, as opposed to conventional Li-ion battery cathodes, most of which are “lithiation-stabilized”. In this study, we combine the cathode voltage information from the Materials Project consisting mostly of lithiation-stabilized structures, and from a newly available dataset with mostly delithiation-stabilized structures. The resulting voltage distributions with respect to redox pairs and anion types for both classes of compounds reemphasize known design principles for high-voltage cathodes, which favor later transition metals in their higher oxidation states and polyaion groups. In addition, a machine learning model for voltage prediction based on chemical formulae is constructed, and shows state-of-the-art performance when compared to two established composition-based ML models for materials properties predictions, Roost and CrabNet.
(Invited) Cell-Level Instrumentation of Lithium-Ion Battery Modules to Support Model Validation
Authors: Preger Yuliya , Mueller Jacob , Fresquez Armando , Rich Chaz
Published: December 2024

Li-ion cells are the building block for most new energy storage systems. Cells are connected in series and parallel to form modules that meet the power and energy requirements of different applications. However, module design and control decisions are often based on electrochemical modeling of single cell cycling results. The predictions of these models do not always match full battery performance because the connection of cells introduces new electrical and thermal phenomena.

Public studies on module cycling are limited: most report limited cycle time, consider only a couple of cells, or lack cell-level monitoring. This talk will review experimental data on the impact of different series-parallel configurations and cell-to-cell variation on module energy throughput, current balancing, and voltage divergence over the course of hundreds of cycles. We will emphasize the impact of parallelization on module performance. This analysis was enabled by cell-level data collection of voltage, current, and temperature. The data provides a foundation to evaluate the performance trade-offs and safety implications of different module configurations, as well as validate electrochemical models of larger battery systems.

SNL is managed and operated by NTESS under DOE-NNSA contract DE-NA0003525.
Accelerating Battery Research and Development through Data-Driven Approaches: The Battery Data Alliance's Battdata Platform
Authors: Concepcion Anthony , Wang Bing Syuan , Subramanian Venkat R. , Pathak Manan , Pathak Chintan
Published: December 2024

In the rapidly evolving landscape of battery research and development, the need for efficient, standardized, and automated testing processes has become increasingly apparent. Battery testing generates an immense volume of valuable electrochemical data, including critical parameters such as cell impedance, coulombic efficiency, capacity, and constant current/constant voltage (CC/CV) charging times. However, the lack of standardization and the limitations of traditional data management approaches have hindered the ability to extract meaningful insights from this wealth of data.

LF Energy's Battery Data Alliance (BDA) is a collaboration between industry leaders, academic institutions, and open-source communities to drive innovation and accelerate the development of advanced battery technologies. [1] BDA has developed BattData, a user-friendly battery data management framework, that enables researchers and engineers to efficiently store, process, analyze, and visualize vast amounts of battery testing data. BattData includes the module BattDB, a time series relational database designed to handle the unique requirements of battery data storage. BattDB acts as a centralized repository, allowing seamless access and querying of specific datasets. Its optimized architecture ensures high performance and scalability, making it capable of handling the ever-growing volume of battery testing data.

To streamline the data ingestion process and ensure data integrity, we introduce BattETL (Extract, Transform, Load), an open-source Python module. BattETL automates the extraction, transformation, and loading of battery cycler data into BattDB, supporting popular cycler platforms such as Maccor and Arbin. By eliminating manual data preprocessing tasks, BattETL significantly reduces the burden on researchers and minimizes the risk of errors.

In this talk, we demonstrate the benefits of the same and introduce other supporting visualization, modeling, and parameter estimation tools.

References

Battery Data Alliance – LF Energy (https://lfenergy.org/projects/battery-data-alliance/)
High Throughput Li-Ceramic Battery Electrolyte Property Optimization Based on Efficient Sample Space Exploration
Authors: Hinricher Jesse , Prein Thorben , Simons Philipp , Kim Kunjoong , Foshey Michael , Tian Yunsheng , Erps Tim , Matusik Wojciech , Olivetti Elsa , Rupp Jennifer L.M.
Published: December 2024

Solid-state batteries (SSBs) are safer, cheaper, and higher energy density alternatives to conventional Li-ion batteries, of which the global production capacity has reached 150 GWh/year. However, development of new materials and their optimization to predicted ideal structure-property characteristics can typically take >10 years of capital-intensive research to find the best solidification and dopant strategies. Typical solid-state manufacture considers high temperature synthesis based on powder sintering for Co-free cathode material integration, which is time and labor intensive, produces a large CO2 footprint, and incurs high costs. In contrast, wet-chemical fabrication methods like sequential deposition synthesis (SDS) can realize faster achievements in direct liquid precursor to solidified electrolyte translation. Li7La3Zr2O12 (LLZO) is a promising solid electrolyte that has seen much interest in the battery field and has been demonstrated to be manufacturable with thickness between 1-15 µm via SDS. In this work, we demonstrate for the first time a high throughput experimental platform, computer-controlled SDS deposition system to rapidly fabricate LLZO and alter its structure-property characteristics with precursor and deposition modulations. Dopant amounts are adjusted in real time to easily control film composition and test a wide parameter field. In-situ annealing is achieved via an advanced heating element implemented directly into the chamber. Raman spectroscopy exhibits sufficient sensitivity to the phases under study, is a contactless and nondestructive technique, and has a low data acquisition time allowing for high-volume analysis coupled to high throughput synthesis. A quantification of Raman spectra is presented to supply a Bayesian optimization framework for greater experimental efficiency by selecting interesting regions by phase and other 2nd order characteristics of the experimental space to selectively sample for desired phases and, importantly, their high-dimensional phase boundaries. The capabilities of the new experimental framework are discussed for further work in accelerated solid-state ceramic materials discovery and we employ here careful considerations on the synthesis methods and computational approaches chosen towards the plethora of ceramic process options. This framework is capable of rapidly synthesizing solid-state electrolytes with a variety of composition and deposition conditions to optimize their properties so they can be rapidly deployed in next-generation batteries in significantly shorter times than traditional methods allow.
Crystal Morphologic Design of Na-Layered Oxide Crystals by Automated High-Throughput Flux Method Experiments
Authors: Shimizu Yusaku , Yamada Tetsuya , Yanagisawa Kazumichi , Tipplook Mongkol , Hayashi Fumitaka , Teshima Katsuya
Published: December 2024

Na
x
CoO2 (NCO), a type of sodium layered oxide, has attracted attention as a cathode material for sodium ion batteries (SIB). The P2-type NCO has a layered structure and is generally obtained as plate-like crystals. Crystal growth of high aspect ratio NCO would be desirable to improve output density. The flux method, which is a liquid-phase method using flux (molten salt) as a solvent, is a crystal growth method that can control the crystal plane. However, in the case of NCO crystal growth, the specific effects of the flux species have not been clarified. In addition, NCO has several phases (O3, P2, P'3, etc.), depending on the Na ratio. However, fluxes easily change the Na ratio in the reaction field. For this reason, experimental guidelines for crystal control cannot be established, and trial and error must be used. The experimental search space considered by the flux method is enormous, with at least 14,000 possible experimental search spaces (three levels of raw material species and heating temperatures, nine raw material Na:Co ratios, 18 flux species and even two-component mixtures are allowed), making an exhaustive search unrealistic. To overcome this issue, it would be necessary to develop a crystal phase diagram expressing the crystal polymorphism and aspect ratio. For this purpose, identification of fluxes effective for P2 phase formation and aspect ratio changes was carried out using a data-driven, wide-area experimental search.

For the data-driven exploration, flux method experiments was conducted using the high-throughput flux method screening (HTFS) system, which was originally developed in our laboratory. In the experiments, the initial training data was collected using a grid search method, and applied to a Bayesian optimization system. In this time, regression model was constructed using the feature values extracted from X-ray diffraction (XRD) patterns as the objective variables and the experimental conditions as the explanatory variables.

About 50 experimental data were collected in a grid search using the HTFS. The formation of P2-type NCO crystals was confirmed in more than half of the samples. However, diffraction lines originating from the impurity Co3O4 were observed in some samples, confirming the strong dependence of the product on the experimental conditions. Data-driven experiments were then undertaken. A total of about 30 experiments were carried out, and in many cases P2-type crystal samples were obtained using Na-based fluxes. In addition, the aspect ratio depended on the flux species and barrel-type crystal morphology was grown in some Na-based fluxes. In this presentation, the mechanism of flux contribution to crystal growth is discussed in terms of solute-solvent interaction and surface energy.

Acknowledgement

This work was partly supported by the Strategic Innovation Program (SIP) of the Cabinet Office, and joint research with companies.
(Invited) A CMOS Inverter Technology Constructed with T-Shaped Gate Poly-Si Thin-Film Transistors
Authors: Lin Horng-Chih , Lee Cheng-Kuei , Li Pei-Wen , Chen Kung-Ming , Huang Guo-Wei
Published: December 2024

In this work, we report a novel CMOS TFT fabrication scheme with the poly-Si TFTs featuring a T-shaped gate (T-gate), air spacers, and a self-aligned lightly doped drain (LDD). The formation of the n+ poly-Si T-gate is based on a unique process we developed recently [1]. Furthermore, by appropriately adjusting the implantation energy and dosage, we can achieve both LDD and heavily doped source/drain (S/D) regions with a single implantation process [2]. After the transistors are fabricated, the deposited oxide layer for passivation presses down on the wings of the T-gates, causing them to droop and naturally form air spacers on both sides of the gate electrodes. This can help reduce parasitic capacitance between the gate and S/D. As expected, the implementation of LDD can reduce the off-state leakage current for both n- and p-channel poly-Si TFTs as compared to the counterparts with the conventional gated configuration and without LDD. In the meantime, the n-channel T-gate poly-Si TFTs with LDD also exhibit a higher on-current than the conventional ones, thanks to the greatly shortened gated length [2]. Nonetheless, the on-current of the p-channel T-gate poly-Si TFTs with LDD is lowered despite the shorter gate length. Details about the mechanisms are explored in this work. Moreover, using n+ poly-Si gates increases the threshold voltage (Vth) of p-channel TFTs. This issue can be addressed by adjusting channel doping. Using this approach, we have successfully fabricated CMOS inverters. Figure 1 illustrates the voltage transfer characteristics (VTC) of fabricated CMOS inverters operated at 3V, confirming full-swing switching. Additionally, adjusting the channel doping of the p-channel TFT effectively tunes the switching voltage, as shown in Fig. 1.

References

[1] Y. A. Huang, C. Y. Liang, K. P. Peng, K. M. Chen, G. W. Huang, P. W. Li, and H. C. Lin, “A unique approach to generate self-aligned T-Gate transistors in counter-doped poly-Si with high etching selectivity and isotropy,” IEEE Electron Device Lett., vol. 41, no. 3, pp. 397–400,

[2] C. K. Lee, K. M. Chen, G. W. Huang, P. W. Li, and H. C. Lin, “Fabrication and characterization of T-gate polysilicon thin-film transistors with lightly-doped drain,” Jpn. J. Appl. Phys., vol. 62, SC 1009-1–5, Dec. 2022, DOI: 10.35848/1347-4065/aca773.

Figure 1
Dose-Dependent Performance of Boron-Implanted Self-Aligned Bottom-Gate IGZO TFTs
Authors: Powell Eli , Kabir Muhammad Salahuddin , Chowdhury Rahnuma Rifat , Manley Robert G. , Zhu Bin , Hirschman Karl D.
Published: December 2024

Self-aligned channel regions in thin-film transistors (TFTs) have advantages in reduced parasitic capacitance and stage delay, and a reduction in overhead real estate. A common method used to fabricate self-aligned a-Si:H TFTs is to utilize a through-glass exposure of photoresist which is blocked by the opaque metal bottom-gate electrode. This process does not require an additional photomask or lithographic alignment, and thus supports low production cost. Sputtered IGZO has been introduced into flat panel display product manufacturing, exhibiting a channel mobility of approximately an order of magnitude higher than a-Si:H. The working source/drain electrodes in IGZO TFTs can be direct metal contact regions to the IGZO, without the need for additional processes such as doping to render the IGZO conductive. Proper metallurgy and annealing processes can provide ohmic behavior with minimal series resistance, however this usually requires several microns of gate-to-source/drain overlap to ensure such behavior.

This work provides an interpretation of donor activation in self-aligned bottom-gate IGZO TFTs with ion-implantation of boron (11B+) species as the source/drain treatment. Passivated staggered bottom gate devices were fabricated with a sputtered 50 nm IGZO channel layer. Following standard passivation processes, devices were implanted adjacent to channel regions with boron doses of 1, 2, and 4×1015 cm-2 at an energy of 35 keV. Utilizing this anneal-implant sequence resulted in self-aligned devices with transfer characteristics comparable to non-self-aligned TFTs.

Upon comparing linear mode device characteristics (Vds = 0.1 V) between the different treatments, a dose dependence was observed. Transfer characteristics appeared similar with a boron dose of 1 and 2×1015 cm-2 however, a 45% decrease in max current was observed for devices implanted with a 4×1015 cm-2 dose. From Van der Pauw measurements, a similar trend was observed; an increase in boron dose resulted in an increase in sheet resistance. A left shift in transfer characteristics was observed following thermal stress at 175 °C which is hypothesized to be the result of an interstitial boron species that remains mobile and provides a relatively low dose throughout the channel during and after thermal stress. An increased boron dose is hypothesized to result in a greater concentration of interstitial boron resulting in a greater degree of transfer characteristic shifting following thermal stress. An alternative hypothesis involves the possibility of hydrogen contamination during the implant process. The results of the investigation on the source of instability will be presented.

Figure 1