Microelectronic Manufacturing Research Articles

A healable amino silane self-assembled monolayer incorporating polymer capped palladium nanoclusters and its application as the copper diffusion barrier layer on silicon wafer

Self-assembled monolayers (SAMs) with short organic chains terminated with specific functional groups are attractive for modifying surface properties for a variety of applications, including being a copper diffusion barrier layer on Si wafer. However, SAM formation is process-sensitive and ideal SAM is difficult to obtain, both of which need to carefully deal with before practical use. Herein we developed a system to achieve seamless electroless deposited copper (ELD Cu) film on an amino-terminated silane SAM modified Si wafer with the help of a bifunctional polyvinyl alcohol-capped Pd nanocluster (PVA-Pd) catalyst. The PVA-Pd acts as a catalyst for ELD Cu as well as a healing agent to fix defects of the amino silane SAM. The sheet resistance, cross-sectional morphology analysis and adhesion of ELD Cu on the Si wafer are extensively studied. By controlling the abundancy of PVA in PVA-Pd, the diffusion barrier property of a flawed amino-terminated silane SAM remains reliably effective even after a 500 °C rapid thermal annealing (RTA), evidencing the superior healing function of PVA. The whole thickness of the PVA-Pd/amino-terminated silane SAM before and after RTA is 〜6 nm and 〜2 nm, respectively, which is applicable for advanced microelectronic manufacturing.

Residual Stress Characterization in Microelectronic Manufacturing: An Analysis Based on Raman Spectroscopy

Residual Stress Characterization in Microelectronic Manufacturing: An Analysis Based on Raman Spectroscopy

DMD maskless lithography optimization based on an improved genetic algorithm

In this paper, we propose an effective method for optimizing mask design using an enhanced genetic algorithm (GA), significantly boosting digital micromirror device (DMD) maskless lithography performance. After a thorough evaluation of various optimization techniques, we determined that the simulated annealing-enhanced GA (SA-GA) offers superior improvements in lithography simulations, thus optimizing mask design more effectively. Our findings reveal that this method achieves up to 88% and 75% enhancement in simulation accuracy for circular and heart-shaped patterns, respectively, surpassing the results of conventional Hopkins lithography simulations. The remarkable effect of improved GA in enhancing the quality of DMD digital lithography shows that it will have great potential in micro-fabrication applications, and paves the way for the realization of high-fidelity and efficient DMD digital lithography technology, which has excellent versatility and adaptability in the field of microelectronics manufacturing.

Terahertz Sensing of Å‐scale Thin Dielectric Film Via Electron Tunnelling

AbstractPrecise sensing of ultrathin dielectric films is paramount in various fields including nanoscale dielectric characterization, advanced material development, and quality control in microelectronics manufacturing. However, achieving this with conventional detection techniques within the terahertz (THz) frequency range has remained challenging due to their limited sensitivity and resolution. In this study, a novel approach is reported that utilizes Fowler‐Nordheim (FN) tunnelling at a metal‐dielectric junction, which drastically improves the sensitivity to changes in dielectric film thickness down to the Angstrom scale. Through a detailed analysis of both experimental and simulated data, it is demonstrated that the FN tunnelling‐based THz sensing technique exhibits exceptional sensitivity. This finding not only overcomes the limitations of traditional detection techniques, but also paves the way for novel ultrathin film sensing capabilities in the rapidly advancing field of THz technology.

Visual Sensor with Host-Guest Specific Recognition and Light-Electrical Co-Controlled Switch.

Perception of UV radiation has important applications in medical health, industrial production, electronic communication, etc. In numerous application scenarios, there is an increasing demand for the intuitive and low-cost detection of UV radiation through colorimetric visual behavior, as well as the efficient and multi-functional utilization of UV radiation. However, photodetectors based on photoconductive modes or photosensitive colorimetric materials are not conducive to portable or multi-scene applications owing to their complex and expensive photosensitive components, potential photobleaching, and single-stimulus response behavior. Here, a multifunctional visual sensor based on the "host-guest photo-controlled permutation" strategy and the "lock and key" model is developed. The host-guest specific molecular recognition and electrochromic sensing platform is integrated at the micro-molecular scale, enabling multi-functional and multi-scene applications in the convenient and fast perception of UV radiation, military camouflage, and information erasure at the macro level of human-computer interaction through light-electrical co-controlled visual switching characteristics. This light-electrical co-controlled visual sensor based on an optoelectronic multi-mode sensing system is expected to provide new ideas and paradigms for healthcare, microelectronics manufacturing, and wearable electronic devices owing to its advantages of signal visualization, low energy consumption, low cost, and versatility.

Trusted Data Acquisition in Microelectronics Manufacturing as a Basis for ML-Optimized Processing

In the digitization of manufacturing processes, one of the major goals is the connection of production facilities and the use of data to digitize business processes. In order to optimize manufacturing processes and maximize the quality of the resulting product, further process information and data directly from the work piece and from the manufacturing environment are required to achieve a holistic system view in addition to the selected data that the manufacturing systems already provide. Within the SiEvEI 4.0 project, a research consortium from industry and academia works on process digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for trustworthy distributed manufacturing and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The basic concepts of this approach have been presented at IMAPS 2021. As an update, this paper reports on the actual experimental evaluation of these concepts in two different assembly lines, including data acquisition, data handling and AI processing with the goal to optimize processing targeting higher production yield and product quality. Specific for this work is the focus on high mix/low volume SMD assembly using fully automated equipment and Solder Ball Application. As a result, the paper presents the experimental validation of the manufacturing process digitization and the use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.

A Review of Development of Chemical Sensors

A comprehensive summary of the most recentre search concentrating on chemical sensors utilizing nano tubes,Nano rods,Nano belts, and nano wiresis given in this article. The experimental principle, sensor device design,sensor mechanism, and multiple significant conclusionsreceivethevastmajorityofourattention.Thefollowingfoursectionsprovide additional information on the creation of chemical sensors based on nanostructured materials: nanotube sensors, Nano rod sensors, Nano belt sensors and Nano wire sensors. Weconcludethisreviewwithpersonalperspectivesonthedirectionstowardswhichfuture research on nanostructured sensors might be directed. At ambient temperature, the nanotube sensors show a far faster response time and a significantly higher sensitivity than the current solid-state sensors. Reversibility of the sensor can be obtained by heating it to a high temperature or by slowly recovering under ambient conditions. Over the past decade, synthesized nanomaterial’s, such as carbon nanotube, nanoparticle, quantum dot, and nanowire, have already made breakthroughs in various fields, including biomedical sensors. Enormous surface area-to-volume ratio of the Nano materials increases sensitivity dramatically compared with macro-sized material. Herein we present a comprehensive review about the working principle and fabrication process of nanowire sensors. Then anotube sensor sex hibit a fast response and asubstantially higher sensitivity than thatofexistingsolid-statesensorsatroomtemperature.Sensorreversibilityisachievedbyslow recovery under ambient conditions or by heating to high temperatures.Synthesized Nano materials, including carbon nanotubes, nanoparticles, quantum dots, and nanowires, have already achieved significant advances in a number of sectors, including biological sensors, during the last ten years. When compared to macro-sized material, the nanoparticles' enormous surface area-to-volume ratio significantly boosts sensitivity. We provide a thorough overview of the fabrication process and operating principle of the nanowire sensor here. Recent advances including self-powering, reusability, sensitivity in high ionic strength solvent,andlong-term stabilityaresurveyedandhighlightedas well.Nanowire isexpectedtolead significant improvement of biomedical sensor in the near future.Advantages and disadvantages of the sensors are provided along with brief descriptions. An electrical device that monitors changes in a quantity, such as voltage, temperature, pressure, or humidity, is called a sensor. As a result, classification is done according to the attribute that a sensor measures. A temperature sensor detects even the smallest variations in its environment or shifts in temperature, such as hot or cold weather. Microelectronic manufacturing techniques have been used to manufacture chemical and electrochemical sensors.Thedevelopment ofchemical sensors has been spurred forward by the recentadvancesinmicromachingtechnology.Chemicalsensorresearchhasadvancedthanksto micro machining methods like sacrificiallayers, plasmaetching, andchemicalanisotropicetching. These methods enable the creation of low mass, low power driven devices as well as controlled working conditions and temperature. Researchersbecameinterestedinthesecarefullychoseninvitronanostructureswhentheywere combined with nanomaterials because the improved properties of these nanostructures led to advances inthe analytical performance of chemical and biosensors. Thisstudycoversthe latest advancements in recognition elements that are integrated in to chemical sensors and bio sensors used in the food, medical, and environmental domains. These elements include both traditional ones like enzymes and antibodies as well as more modern ones like aptomers and phages.Cyclic voltammetry and electron microscopy are frequently used in the characterization of chemically modified sensors and biosensors because they enable the confirmation of electrode mechanisms and surface morphologies. X-ray photoelectron spectroscopy (XPS), among other methods, is a special tool for obtaining information on the sensor surface that is qualitative, quantitative/semi-quantitative, and speciation-related. However, XPS is still not widely applied in this industry. The purpose of this work is to review a few selected studies that demonstrate how wellXPSperformswhencharacterizingthetopsurfacelayersofbiosensorsandchemically Modified sensors. The reader is provided with the necessary background information in a brief introduction to X-ray photoelectron spectroscopy. The use of XPS to characterize sensors appropriate for environmental and food analysis is highlighted.

Unsupervised Representation Learning for Large-Scale Wafer Maps in Micro-Electronic Manufacturing

Recognition of wafer map defect patterns is essential for evaluating the reliability of micro-electronic manufacturing. Due to the difficulty of labeling, the available large-scale wafer maps are raw data without labeling. The scarcity of labeled samples reduces the defect pattern recognition accuracy of popular deep convolutional neural networks. To overcome this problem, we propose a wafer map deep clustering (WMDC) model. It learns generic representations from unlabeled datasets in an unsupervised manner. A prototype metric loss during training helps to learn the semantic features of the categories. We improve the recognition accuracy of the model when trained using scarce labeled data by transferring the weights of unsupervised pretraining. Experiments on WM811K and MixedWM38 wafer datasets demonstrate that the WMDC model is capable of obtaining robust prior representations from the unlabeled wafer maps. Accuracies of 97.43% and 98.74% are obtained when fine-tuning using scarce labeled data from both datasets, respectively.

A Novel Hierarchical Tree-DCNN Structure for Unbalanced Data Diagnosis in Microelectronic Manufacturing Process

A Novel Hierarchical Tree-DCNN Structure for Unbalanced Data Diagnosis in Microelectronic Manufacturing Process

Technologijos ir medžiagos, įgalinusios elektronikos elementų miniatiūrizaciją

This article introduces the technologies used in microelectronics manufacturing (integrated circuits, printed circuit boards, etc.) It reviews traditional semiconductor materials (germanium, silicon) and some of the best the most famous and promising, but currently still being researched, semiconductor materials (carbon nanotubes, graphene). The article briefly describes Lithuania's potential in the production of microelectronic devices. Key words: semiconductor, nanotubes, integrated circuits, printed circuit boards, PIC.

Wafer Image Preprocessing Based on Density Based Spatial Clustering of Application with Noise

With the development of microelectronic manufacturing technology, semiconductor manufacturing presents the trend of maximization of scale and miniaturization of process size. Even if the wafer production now has a high automation of the production process, high-precision production equipment and advanced production technology, wafer abnormal situation is still inevitable. Abnormal conditions in semiconductor manufacturing process will reduce the yield of wafer products and increase production costs. The later analysis becomes the necessary means to improve the wafer yield. With the rapid development of modern computing power, the application of machine learning-based automatic detection method in semiconductor production is irresistible. In addition to its spatial pattern, there are many noises that can affect the classification of defects, so it is necessary to preprocess the wafer diagram. The traditional density based spatial clustering of application with noise (DBSCAN) algorithm needs to determine two clustering parameters artificially, and the choice of parameters can affect the clustering effect easily, the parameter list was obtained by K-mean nearest neighbor algorithm and mathematical expectation method, and the integrated parameters of intra-cluster density and inter-cluster density after DBSCAN clustering were selected as evaluation indexes to select the optimal parameters. Finally, a self-adaptive (SA)-DBSCAN map is obtained by retaining the largest cluster, adding feature points and feature clusters, thus improving the yield of wafer.

On-Chip Monolithically Integrated UltravioletLow-Threshold Plasmonic Metal-Semiconductor Heterojunction Nanolasers.

The metal-semiconductor heterojunction is imperative for the realization of electrically driven nanolasers for chip-level platforms. Progress in developing such nanolasers has hitherto rarely been realized, however, because of their complexity in heterojunction fabrication and the need to use noble metals that are incompatible with microelectronic manufacturing. Most plasmonic nanolasers lase either above a high threshold (101 -103 MW cm-2 ) or at a cryogenic temperature, and lasing is possible only after they are removed from the substrate to avoid the large ohmic loss and the low modal reflectivity, making monolithic fabrication impossible. Here, for the first time, record-low-threshold, room-temperature ultraviolet (UV) lasing of plasmon-coupled core-shell nanowires that are directly grown on silicon is demonstrated. The naturally formed core-shell metal-semiconductor heterostructure of the nanowires leads to a 100-fold improvement in growth density over previous results. This unprecedentedly high nanowire density creates intense plasmonic resonance, which is outcoupled to the resonant Fabry-Pérot microcavity. By boosting the emission strength by a factor of 100, the hybrid photonic-plasmonic system successfully facilitates a record-low laser threshold of 12kW cm-2 with a spontaneous emission coupling factor as high as ≈0.32 in the 340-360nm range. Such architecture is simple and cost-competitive for future UV sources in silicon integration.

Preface

The International Conference on Aerospace, Aerodynamics and Mechatronics Engineering has been successfully held two times, and has attracted various promising researchers who are developing new directions in the field of aerospace, aerodynamics and mechatronics engineering.The 2023 2nd International Conference on Aerospace, Aerodynamics and Mechatronics Engineering (AAME 2023), held in Beijing, China from April 21st to 23rd, 2023, is meant to unite researchers, experts, professors and academic staff as well as a wide range of participants of professional community from China and other countries concerned. AAME 2023, with more than 100 participants, was held to further enhance aerospace technology and its application as well as the potential business or research contact among all the participants.The highly eventful program of the Conference, the list of well-established organizations-participants and devoted engagement of many colleagues throughout all the stages of the Conference’s preparation instill confidence in practical importance of mutual initiative. The framework of the Conference comprises keynote speeches, oral presentations, and academic investigation, discussing open and significant problems facing aerospace, aerodynamics and mechatronics engineering, proposing innovative ideas and approaches to solution of the problems, and considering new possibilities of application and development of cutting-edge aerospace technology.The Conference has become one of the venues for demonstrating scientific achievements of international class by leading scientists and young scholars and would facilitate the strengthening of academic cooperation, meanwhile the Conference proceedings would be expressed in the form of international scientific publishing. Various topics of the papers, gone through a vigorous peer review process, are covered in the proceedings, including: Rocket Theory and Design, Developments in New Space Communication Techniques, Aircraft Navigation and Positioning Technology, Microelectronics Manufacturing Engineering Signal Processing, Vacuum Electronic Technology, etc.On behalf of the Conference Organizing Committee, we would like to thank the referees for their efficient and thoughtful actions. We are grateful to the members of the Technical Program Committee for their efforts in making and shaping the program for AAME 2023. Particularly, we acknowledge the publishing support from the members of Journal of Physics: Conference Series.We hope that the future AAME will be as successful and stimulating, as indicated with the contributions presented in this volume.The Committee of AAME 2023List of Committee Member is available in this pdf.

Understanding palladium-tellurium cluster formation on WTe2: From a kinetically hindered distribution to thermodynamically controlled monodispersity.

A fundamental understanding of the transition metal dichalcogenide (TMDC)-metal interface is critical for their utilization in a broad range of applications. We investigate how the deposition of palladium (Pd), as a model metal, on WTe2(001), leads to the assembly of Pd into clusters and nanoparticles. Using X-ray photoemission spectroscopy, scanning tunneling microscopy imaging, and ab initio simulations, we find that Pd nucleation is driven by the interaction with and the availability of mobile excess tellurium (Te) leading to the formation of Pd-Te clusters at room temperature. Surprisingly, the nucleation of Pd-Te clusters is not affected by intrinsic surface defects, even at elevated temperatures. Upon annealing, the Pd-Te nanoclusters adopt an identical nanostructure and are stable up to ∼523 K. Density functional theory calculations provide a foundation for our understanding of the mobility of Pd and Te atoms, preferential nucleation of Pd-Te clusters, and the origin of their annealing-induced monodispersity. These results highlight the role the excess chalcogenide atoms may play in the metal deposition process. More broadly, the discoveries of synthetic pathways yielding thermally robust monodispersed nanostructures on TMDCs are critical to the manufacturing of novel quantum and microelectronics devices and catalytically active nano-alloy centers.

Microminiature Pressure Sensors Based on Diode Structures

The paper analyzes modern microminiature pressure sensors made on various diode structures, in particular on organic light-emitting diodes, field-effect transistors, photovoltaic elements and multi-circuit piezoresistive sensors. The possible areas of application of such sensors, their main advantages and disadvantages are shown. The study of 4 groups of samples of diode heterostructures based on CdS / ZnS / CuS / CdTe was carried out and the perspective of using pressure sensors based on these materials as an analogue of existing semiconductor devices was shown. On the basis of experimental studies with the application of pressure, twisting and illumination, it is substantiated that these structures are piezoelectric. The complete technological process of the step-by-step creation of these structures is presented. The obtained structures were analyzed: structural diagrams, current-voltage and piezoelectric characteristics in comparison with the characteristics of other piezoelectric materials are given. Possible areas of application of such structures are described. The provided design schemes and parameters of the obtained diode structures may be of interest to a wide range of specialists in the field of sensor technology and automation of various technological processes of microelectronic equipment manufacturing. It is shown that by changing the sensor manufacturing technologies and the concentration of chemical elements in the obtained films, it is possible to change the sensitivity of the sensor and the dynamic range of its operation, adapting the sensor parameters to the field of its application in the relevant measuring electronic equipment and pressure control systems.

Highly sensitive and fast response Love wave mode humidity sensor based on MoS2@Ti3C2Tx hierarchical structure

Humidity sensing is crucial for food production, environmental monitoring, microelectronic manufacture, and common living conditions. Thus, there is a high demand for development of high-performance humidity sensors with good stability, high precision and fast responses. In this study, Love wave mode humidity sensors were prepared using quartz-based SAW devices coated with composite sensing layers of accordion-like Ti3C2Tx, MoS2 nanoflowers and MoS2 @Ti3C2Tx hierarchical structure, respectively. The hydrophilic Ti3C2Tx was used to provide abundant surface functional groups for adsorption of water molecules, and the hydrophobic MoS2 enabled rapid adsorption and desorption of water molecules from the surface of MoS2 @Ti3C2Tx. The MoS2 @Ti3C2Tx coated Love wave humidity sensor presented an ultra-high sensitivity of 0.67 kHz/% RH in the wide humidity range from 11.3% RH to 97.3% RH, mainly attributed to the hierarchical structure of MoS2 @Ti3C2Tx with large specific surface areas, abundant adsorption sites and superhydrophilicity. Meanwhile, this Love wave humidity sensor showed fast response/recovery rates, great repeatability and long-term stability, indicating its great prospects in humidity detection applications.

Foundations of machine learning for low-temperature plasmas: methods and case studies

Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

A dissipative and entropy-optimized MHD nanomaterial mixed convective flow for engineering applications.

Background and objective: Nanomaterials play significant roles in numerous industrial and engineering applications, like nuclear plants, paper production, thermal power plants, glass fibres, manufacturing of medicines, medical instruments, micro-electronics and polymer sheet extrusion. In view of such important applications, in this study, we discuss the magnetohydrodynamic flow of a nanofluid over an inclined surface by employing the Darcy-Forchheimer model. The Buongiorno model is applied to understand the various important aspects of the nanofluid. Radiation, magnetic field, dissipation and entropy generation in a chemically reactive flow are also discussed. Methodology: The governing nonlinear expressions were transformed into a dimensionless system through adequate transformations. The obtained non-dimensional systems were computed by the NDSolve approach. Results: Physical illustrations for the flow, temperature, concentration and entropy rate via emerging variables were examined. Here an enhancement in velocity was seen for the mixed convection variable, while opposite impacts on flow and temperature were noticed through the Hartman number. A higher Eckert number was obtained with a rise in temperature, while a decrease in concentration was noticed for the thermophoresis variable. An augmentation in the entropy rate was detected for radiation, while the thermal transport rate was boosted by thermophoresis.

Wet scandium etching for hard mask formation on a silicon substrate

Nowadays, microelectronics requires the search for materials, including masks for creating structures. The intermediate hard mask strategy is one of the key issues in achieving a good balance between lithography and etching in microelectronics manufacturing. One of the interesting challenges in microelectronics and photovoltaics is the creation of interlayer, vertically oriented silicon arrays on Si substrate for semiconductor devices with multifunctional purposes. The fabrication of such structures is still a serious technological problem and requires searching for approaches and materials. In this work, we propose using scandium as a hard mask material over silicon due to its high resistance to plasma chemical etching and low sputtering coefficient. We have shown that a wet etching of the scandium layer with a thickness of several nanometers can be used to obtain pattern structures on silicon with a resolution of up to 4 µm, which is a good result for the wet etching approach. The scandium was found to be an excellent resistant mask over silicon with the lowest etch rate compared to other metal masks under the selected plasma etching conditions. Therefore, a scandium hard mask can open up possibilities for the formation of different microscale topographical patterns.

Superconformal Deposition for Building 3D Circuitry

State of the art manufacturing of microelectronics involves the electrodeposition of copper for fabricating on-chip interconnects, through-silicon-vias for chip stacking, and high density printed circuit boards. This lecture will present a selection of highlights related to superconformal electrodeposition of Cu ranging from its history to its impact on processing of microelectronic circuitry and, more generally, the understanding of additive effects in electrodeposition. The story begins in the mid-20th century with the formal discovery of the effectiveness of plating additives in yielding smooth surfaces i.e. brighteners and levelers, before pivoting to the IBM paradigm changing introduction of complex 3-D Damascene Cu superfilling for on-chip interconnects in 1997. The role of electroanalytical, metallurgical, and surface science measurements along with insights, and modeling to develop a mechanistic understanding of morphological and microstructural evolution during trench and via filling will be detailed. The lessons learned and their extension to other chemical systems and applications will be discussed along with the challenges that lie ahead.