bluuebunny/crossref_metadata_2025_split huggingface dataset:
(Invited) Hexagonal Silicon Germanium As a Direct Bandgap Light Source
Authors: Peeters Wouter H.J. , van Lange Victor T. , van Tilburg Marvin A.J. , Belabbes Abderrezak , Jansen Marvin , Farina Riccardo , van Hemert Max C. , Vettori Marco , Verheijen Marcel A. , Botti Silvana , Bechstedt Friedhelm , Haverkort Jos E.M. , Bakkers Erik P.A.M. 
Published: December 2024

All SiGe alloys in the cubic crystal phase have an indirect bandgap. Therefore, SiGe alloys are typically considered as unusable as a light source. However, if the Si1-xGex atoms are arranged in a hexagonal crystal structure, the bandgap becomes direct for x>65%. The emission occurs with a nanosecond lifetime, and is tuneable from 1.8 to 3.4um by varying the SiGe composition. The hex-SiGe alloys have a relatively large separation between the direct and indirect conduction band minima, which makes it stand out as a Group IV light source [1]. The hex-SiGe alloys are realized as shells in a core-shell nanowire geometry, using wurtzite GaAs cores as epitaxial templates to copy the hexagonal stacking to the SiGe shells.
Recently, photoluminescence studies of these core-shell nanowires resulted in the observation of amplified spontaneous emission (ASE) from hex-Si0.2Ge0.8, highlighting a positive material gain in this material system. The positive material gain is enabled by the direct bandgap of hex- Si0.2Ge0.8.
Moreover, we have grown nanowires with multiple shells of different Si1-xGex compositions. Thus, these shells contain heterostructures of hex-Si1-xGex, having a type-I band alignment, allowing us to create hex-Ge/Si0.2Ge0.8 quantum wells with light emission up to room temperature.
The observation of ASE, in combination with the ability to create type I QWs from hex-Si1-xGex, pave the way towards a laser based on hex-Si1-xGex alloys.
[1] Fadaly, E. M. T., Dijkstra, A., Suckert, J. R. et al (2020). Direct-bandgap emission from hexagonal Ge and SiGe alloys. Nature, 580(7802), 205-209.
(Invited) Heterogeneous Integration of IR Detectors with CMOS and Thin Film Transistors
Authors: Lo Yuhwa , Jiang Yunrui , Arya Shaurya , Peng Hexuan 
Published: December 2024

Colloidal quantum dots (CQDs) show promising performance in IR detection because of their many unique advantages, including high material quality, flexibility in operation wavelength, easy fabrication process and suitability for heterogeneous integration with CMOS. Our presentation will cover three areas about colloidal quantum dot detectors: (a) the physical model of CQDs, (b) integration of CQDs with electronics, and (c) strategies of overcoming the limit of dark current for uncooled IR detection.
For (a), we will present a physical model considering the unique transport properties of ligand connected quantum dot detectors. The model shows how the CQD detectors perform differently than conventional p-i-n detectors and why those popular parameters derived from Shockley’s diode model such as reverse saturation current and ideality factor cannot model the CQD detectors. For (b), we will show how CQD detectors can be integrated with IGZO thin film transistors on glass substrate, GaN LEDs for wavelength converters, and Si CMOS in a back-end-of-line process. For (c), we will discuss designs of colloidal quantum dot detectors for longer IR wavelengths with minimal effects of dark current to support uncooled operation.
(Invited) Theory-Informed AI/ML for Battery Characterization
Authors: Chan Maria 
Published: December 2024

The use of advanced, including in situ and operando, microscopy and spectroscopy techniques has enabled detailed characterization of battery materials during synthesis and cycling. Data-driven approaches, especially guided by computational spectroscopy, enable robust and accelerated information extraction from such microscopy and spectroscopy data. In this talk, we discuss the featurization of x-ray absorption near edge structure (XANES) for extraction of local structure and electronic properties in NMC battery cathode materials [1], and the extension thereof towards the detection of antisite defect and oxygen vacancy from multimodal EELS/XANES. We also discuss the use of data-driven approaches to accelerate the acquisition [2] and analysis [3] of XANES mapping data. Finally, we discuss the use of AI to extract relevant labeled microscopy and spectroscopy data from scientific literature [4], and recent extensions involving the use of large language models and multi-model contrastive learning.
[1] Y. Chen, C. Chen, I. Hwang, M. J. Davis, W. Yang, C.J. Sun, G. Lee, D. McReynolds, D. Allan, J. M. Arias, S. P. Ong, and M. K. Y. Chan, “Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization,” Chemistry of Materials, 36, 5, 2304–2313 (2024).
[2] S. Tetef, A. Pattammattel, Y. S. Chu, M. K. Y. Chan#, G. T. Seidler, “Accelerating nano-XANES imaging via feature selection,” Digital Discovery 3(1), 201-209 (2024).
[3] S. Tetef, A. Pattammattel, Y. S. Chu, M. K. Y. Chan#, G. T. Seidler, “Manifold Projection Image Segmentation for Nano-XANES Imaging,” APL Machine Learning 1, 046119 (2023).
[4] E. Schwenker, W. Jiang, T. Spreadbury, N. Ferrier, O. Cossairt, M. K. Y. Chan, “EXSCLAIM! -- Harnessing materials science literature for labeled microscopy datasets,” Patterns 4, 100843 (2023).Corresponding author information
Leverage Battery Research with Atomistic Modeling and Machine Learning
Authors: Onofrio Nicolas , Hellström Matti , Spiering Paul 
Published: December 2024

In the quest for sustainable, high-performance energy storage, the development of advanced batteries is crucial. Addressing the complexities of battery materials and processes requires novel, data-driven research approaches. The Amsterdam Modeling Suite (AMS) offers a robust framework for simulating battery materials at multiple levels of theory, integrating atomistic engines (DFT, DFTB, ReaxFF, ML potentials) with a central driver for exploring the potential energy surfaces (PES) via molecular dynamics and Grand Canonical Monte Carlo simulations. The combination of the AMS driver with its dedicated Python interface enables the automatic screening of materials to optimize critical battery properties such as intercalation potentials, diffusion constants, activation energies, redox potentials, mechanical properties, etc.
A key innovation of AMS2024 is its platform for machine learning interatomic potentials, enhancing simulation accuracy while conserving low computational resources. With ParAMS, you can train a single or a committee of machine learning potentials, from scratch, fine-tune universal models, or actively learn the PES based on target molecular dynamics simulation. This facilitates rapid prediction of material properties and reaction mechanisms, accelerating the design and testing of new battery technologies.
Our data-driven approach empowers the battery R&D community to overcome traditional development barriers, enabling precise material performance predictions and the discovery of new materials and mechanisms towards realizing high-energy, stable, and safe energy storage systems.
(Digital Presentation) 2D Transition Metal Borides: Different Etchants for Al Removal from MAB and Application in Cd2+, Pb2+, Cu2+ and Hg2+ Sensing
Authors: Wei Shudan , Kale Girish M , Lai Xiaojun 
Published: December 2024

Despite significant progress in MBene research, achieving perfect layered structures and high purity under different etching conditions remains challenging. Additionally, their potential for heavy metal ion detection in electrochemical applications is underexplored. The synthesis of MBenes, particularly MoB, involves removing Al precursor MoAlB. Recent advancements focus on high-temperature solid-state etching using Lewis bases or chemical wet etching, with wet etching proving more economical, milder, and higher purity products. This study compares various non-fluoride etchants' effects. Ammonium persulfate (APS) achieved a significant etching rate of ~71% for Al, resulting in restacked accordion-like multilayered MBene. However, high temperatures led to stable, insoluble AlxO, reducing product purity. CuCl₂ etching yielded a lower rate of ~54%, producing flake structures with copper impurities. Acid etching resulted in a perfect layered structure but lower purity, while alkali etching provided similar etching rates to APS with the highest purity. Complete Al removal at elevated temperatures caused the loss of the layered structure. Maintaining a small amount of Al during hydrothermal etching supports the layered structure, and subsequent room-temperature etching produces fluffy open-layered MBene MoB (OL-MBene).
Electrochemical studies revealed that OL-MBene-modified GCE exhibited excellent performance in detecting Cd²⁺, Pb²⁺, Cu²⁺, and Hg²⁺ ions individually and simultaneously, with high sensitivity and selectivity. Notably, the sensor exhibited superior performance for Cu²⁺ and Pb²⁺ ions. The varying adsorption energies of these ions explain the differences in sensitivity and selectivity, and Pb²⁺ demonstrated the highest sensitivity due to its lowest adsorption energy on the MBene surface. Competitive adsorption studies indicated that Pb²⁺ competes with Hg²⁺ for adsorption sites while having minimal impact on Cd²⁺ and Cu²⁺ adsorption. This research advances our understanding of MBene synthesis and characterization, introducing a new avenue for leveraging MBenes in sensitive and selective electrochemical sensors for environmental monitoring.

Figure 1
Effects of Ge and B Concentration in Selective Epitaxial Growth of Si1-XGex(:B) at Low-Temperature
Authors: Shin Hyerin , Yoon Dongmin , Ko Dae-Hong 
Published: December 2024

As two-dimensional devices approach their scaling limits, monolithic complementary field-effect transistors have emerged to meet the demands of advanced transistors. A low thermal budget process is essential to prevent damage to the underlying layers while forming upper layers. Therefore, a low-temperature approach for the selective epitaxial growth (SEG) of SiGe(:B) in source-drain formation is necessary. The cyclic deposition and etch method was employed for its high selectivity, highlighting the need for further research into the incubation characteristics of B-doped SiGe. [1] We investigated the incubation behavior of SiGe(:B) on SiO2 under varying Ge and B concentrations in an ultra-high vacuum-chemical vapor deposition (UHV-CVD) chamber.
In this study, we investigated the influence of the Ge and B concentrations on the incubation time for SiGe(:B) on SiO2, comparing it with the epitaxial growth on Si(100) and Si(111). First, we analyzed the incubation properties of undoped Si1-xGex (0 < x < 0.4) layers. We then explored the effect of B concentration by varying the B flow rate. Our findings indicate the higher Ge concentrations extend the incubation time, independent of the total gas flow rate. In contrast, B doping reduces the incubation time compared to with that of undoped SiGe, and increasing B concentration further shortens the incubation time. Atomic force microscopy and scanning electron microscopy were used to examine amorphous formation on SiO2. In addition, high-resolution X-ray diffractometry and ellipsometry were used to analyze Ge and B concentrations, as well as growth thickness. Our study offers comprehensive insights into the low- temperature SEG process window for SiGe(:B).

Acknowledgment

This work was supported by the Technology Innovation Programs (RS-2023-00235609) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).

References

[1] Hartmann, et al. , -ECS Journal of Solid State Science and Technology 3.11 (2014): P382.
Battery Life Prediction Under Few-Shot Conditions
Authors: Wan Jiayu 
Published: December 2024

Accurate prediction of battery lifetime is critical for ensuring timely maintenance and safe operations of batteries. While data-driven methods have made significant progress in battery life prediction, the lack of labeled data often leads to low model accuracy, hindering the development of these techniques. To address this challenge, we develop a semi-supervised learning technique named partial Bayesian co-training (PBCT) to enhance the modeling of battery lifetime prediction. By leveraging the low- cost unlabeled data, our model extracts hidden information to improve the understanding of the underlying patterns in the data and achieve higher accuracy in predicting battery lifetime. Our experiments demonstrate that the proposed method outperforms existing approaches by up to 19.7% on battery lifetime prediction accuracy with negligible overhead for data acquisition. Moreover, our research suggests that incorporating unlabeled data into the training process can help to uncover critical factors that impact battery lifetime, which may not be apparent with a limited amount of labeled data alone. The proposed semi-supervised PBCT approach sheds light on the future direction for efficient and explainable data-driven battery status estimation.
Reference:
1. N. Guo#, S. Chen#, J. Tao*, Y. Liu*, J. Wan*, X. Li*, "Semi-supervised Learning for Explainable Few-shot Battery Lifetime Prediction", Joule, 2024, 8, 1-17
2. Y. Liu*, S. Chen, P. Li, J. Wan*, X. Li*, "Status, Challenges, and Promises of Data-Driven Battery Lifetime Prediction under CPS Context", IET Cyber-Physical Systems: Theory & Applications, 2024, 1-11
Enhancing a-IGZO Thin-Film Transistors Performance Via Multi-Parameter Machine Learning
Authors: Yang Hyunkyu , Kim Youn Sang 
Published: December 2024

Driven by miniaturization in accordance with Moore's Law, memory devices like DRAM and NAND have achieved significant performance improvements. However, this relentless scaling has encountered fundamental physical limitations, hindering further miniaturization of traditional silicon (Si)-based transistors and restricting further performance gains. To confront this challenge, research is actively exploring new materials and architectures. Among these promising candidates, the Indium Gallium Zinc Oxide (IGZO) channel transistor has emerged as particularly attractive due to its advantages in low leakage current. Despite the advantages of IGZO for low-power consumption, achieving high field-effect mobility, crucial for fast operation speeds in memory devices, remains a significant challenge.
To overcome this limitation, a novel machine learning (ML)-based approach is proposed for fabricating high-performance, ultra-thin IGZO thin-film transistors (TFTs) using sputtering process for the channel. This method optimizes the sputtering process, resulting in IGZO TFTs with outstanding multiple electrical properties simultaneously. The fabricated TFTs exhibit a high field-effect mobility of 33.2 cm²/V·s, a near-zero threshold voltage of -0.05 V, and a channel with a sub-10nm thickness. The performance of the fabricated TFTs is comparable to that of transistors produced by Atomic Layer Deposition (ALD), a technique renowned for its ability to create ultra-thin films with high quality. This achievement implies the potential of sputtering process to overcome the throughput challenges of ALD. Leveraging machine learning's strengths, the proposed method guides the optimization of the sputtering process, eliminating the need for traditional labor-intensive and trial-and-error methods. This approach has the potential to significantly accelerate the development of next-generation memory devices with unprecedented efficiency and speed.
IGZO TFT Backplane Integration for µLED Flat-Panel Display
Authors: Powell Eli , Woodard Trevor , Li Zhi , Corbett Brian , Garner Sean , Manley Robert G. , Hirschman Karl D. 
Published: December 2024

Display technologies have continuously evolved since the advent of cathode ray tube (CRT) displays in the early 1900s. Thanks to modern advancements, current display thicknesses are on the order of centimeters and their area has increased 10-fold. Modern flat panel display (FPD) systems consist of a TFT backplane that controls the filtering of a backlight or the modulation of emissive devices such as OLED or microLEDs (µLEDs). MicroLED technology is favored over organic light-emitting diode (OLED) technology due to its higher brightness, energy efficiency, and longer operational lifetime. Additionally, µLEDs offer small pixel sizes, scalability to large screen sizes, and increased durability compared to OLEDs. While both technologies have their strengths and weaknesses, the choice between them depends on specific application requirements and preferences.
The primary focus of this work is the process integration of Indium Gallium Zinc Oxide (IGZO) transistors as a µLED backplane for row/column addressing. A single pixel is composed of a µLED driven by an arrangement of two transistors and a storage capacitor. The pixels are then arrayed on a glass substrate to support active-matrix control of monochrome and full color (RGB) displays from 1x1cm (50 x 50 pixels) up to 7.6 x 7.6 cm (380 x 380 pixels). Optimization of circuit parameters considering size and scan frequency was modeled using existing TFT and µLED electrical device compact models. New process parameters and procedures were determined for the proper integration of µLEDs in an existing TFT fabrication process.
Red, green, and blue µLED devices were fabricated at Tyndall National Institute on native substrates and were transferred to TFT pixels using an X-Display MTP-1003 micro-transfer printer. The performance of individual test cells was assessed using an Agilent B1500, revealing a voltage transfer characteristic indicating the ability to modulate and control µLED current. Techniques were investigated for the system integration of the active matrix with control circuitry.

Figure 1
(Invited) Design, Synthesis and Applications of Two-Dimensional π-Conjugated Polymers
Authors: Perepichka Dmytro F. 
Published: December 2024

π-Electron conjugation in graphene gives rise to unusual electronic properties including massless charge carriers. In principle, some of these properties could be harvested in organic two-dimensional (2D) conjugated polymers which would open up the design space and tunability that are not available to inorganic 2D materials.[1] I will present our progress in synthesis of such structures as atomically thin layers on metallic surface,[2] and as bulk layered structures (2D Covalent Organic Frameworks),[3-5] via dynamic solution polymerization. I will discuss the structure-optoelectronic properties relationships in these materials and the perspectives for their applications in semiconducting devices.
[1] DF Perepichka, F Rosei, Science
2009, 323, 216.
[2] G Galeotti et al, Nat. Mater.
2020, 19, 874. D Dettmann et al, ACS Nano
2024, 18, 849.
[3] T Jadhav et al, Angew.
Chem. Int. Ed. 2019, 58, 13753. T Jadhav et al, J. Am. Chem. Soc.
2020, 142, 8862.
[4] V Lakshmi et al. J. Am. Chem. Soc.
2020, 142, 2155. E Hamzehpoor et al., in preparation.
[5] E Hamzehpoor et al. J. Am.
Chem. Soc.
2021, 143, 13274. E Hamzehpoor et al. Nat. Chem. 2023, 15, 83.
(Invited) Liquid Metals for Creating Optical and Electronic Devices
Authors: Kalantar-Zadeh Kourosh 
Published: December 2024

Liquid metals (LMs) constitute a group of low melting point metals and alloys that blend metallic and liquid properties. Essentially, they represent electronic liquids with intricate interfacial chemistry and physics. The softness, electrical and thermal conductivity, and distinctive interfacial chemistry of LMs have drawn significant attention across various research disciplines. These attributes render them promising materials for catalyzing chemical reactions on their surfaces to design nanoarchitectonic systems, as well as for developing diverse physical units, particularly those applicable in developing optical and electronic devices. The synergy of LM for creating semiconducting, conducting, and isolating materials in electronics is particularly unique.
The utilization of LMs as reaction media to create optical and electronic devices is gaining momentum as a paradigm. These devices are constructed from materials formed at the interface of LMs, which are either harvested from or encapsulated within the core of LM and extracted. This presentation describes such LM based reaction media and provides key examples of LM-based processes for producing functional materials. The discussion encompasses an overview of the synthesis and applications of LM-based materials in various electronic and optical applications, as well as sensing devices, illustrating their use across different physical and chemical systems. The unique features of LMs that render them promising materials for such processes will be highlighted, followed by relevant case studies and demonstrations of a variety of examples.
(Invited) Advancing Quantitative, Contactless, Non-Destructive Characterization of Photonic and Plasmonic Devices
Authors: Zapien J Antonio , Foo Yishu , Phoo May Thawda , Huqe Md Rashedul , Zhang Yun , Kwong Yee Man 
Published: December 2024

Non-destructive, contactless, and fast optical spectroscopy and imaging strategies provide great advantages for the modelling and optimization of thin films and nanostructured materials in photonic and optoelectronic applications. In particular, accurate, high-precision determination of the dispersion properties of sub-wavelength materials remains challenging and overlooked despite its massively demonstrated need and benefits to the semiconductor industry. Over the years, our group has used these strategies to study self-assembled nanolasers, hybrid photonic-plasmonic sensors, and photovoltaic devices. To adapt to these challenges we develop a modelling strategy based on Finite-Difference Time-Domain (FDTD) due to its flexibility and generality that makes it suitable to model non-linear and photo-thermal effects. The advantages of FDTD include i) the ability to obtain a wide spectral response from a single time-domain simulation and ii) its capability to model non-periodic structures. Significantly, we demonstrated that quantitative FDTD modelling can achieve sub-monolayer precision thus matching the capabilities of spectroscopic ellipsometry (SE). In turn, SE stands out from other optical techniques because, as a self-referenced and phase sensitive strategy, its demonstrated precision and sensitivity and has been demonstrated in multiple spectral ranges and operation conditions. We will present and discuss the synergic use of the FDTD-SE strategy as a quantitative tool for the characterization of photonic and plasmonic subwavelength structures of interest in optoelectronic, sensing, and energy applications.
We gratefully acknowledge the financial support from the RGC (Projects CityU - 11210218, 11219919, 11215121 and 11310122) and ITC (Project ITS/461/18) of HKSAR, China.