In Situ Rapid Fabrication of Graphene-Copper Heterojunctions Using Fiber Laser Direct Writing

Multifunctional three-dimensional heterostructures for flexible electronics have gained significant attention due to their distinctive structural formability and superior electronic and optoelectronic properties. Nevertheless, conventional fabrication techniques have yet to be optimized for flexible substrates. In this study, a straightforward fiber laser direct writing (FLDW) process is demonstrated for the simultaneous fabrication of diodes (PN junctions) and bipolar junction transistors (BJTs) on flexible polyimide substrates, which is realized through the deposition of multifunctional p- or n-type copper oxide films (CuOx) and p- or n-type porous laser-reduced graphene oxide films (LrGO) using fiber laser ablation and deposition. The presence of both p- and n-type semiconductor films is confirmed through material characterization. The fabricated PN junctions exhibit reasonable diode rectification ratios, ranging from 20 to 220, and perform reliably under numerous operating conditions such as light-dark illumination and elevated temperatures. Furthermore, I-V curve analysis indicates that the current gain and electrical performance of printed negative-positive-negative (NPN) (or positive-negative-positive, PNP) BJTs can be tailored by adjusting the laser energy density of the FLDW process and the base gap width of the BJTs. As a proof of concept, the FLDW process is successfully employed to deposit both NPN (or PNP) BJTs composed of LrGO/CuOx heterostructures with controlled current gains. Its ease of operation, versatility, and cost-effectiveness make FLDW promising for large-scale flexible electronics fabrication.

Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment

Amid the ongoing emphasis on reducing manufacturing costs and enhancing productivity, one of the crucial objectives when manufacturing is to maintain process tools in optimal operating conditions. With advancements in sensing technologies, large amounts of data are collected during manufacturing processes, and the challenge today is to utilize these massive data efficiently. Some of these data are used for fault detection and classification (FDC) to evaluate the general condition of production machinery. The distinctive characteristics of semiconductor manufacturing, such as interdependent parameters, fluctuating behaviors over time, and frequently changing operating conditions, pose a major challenge in identifying defective wafers during the manufacturing process. To address this challenge, a multivariate fault detection method based on a 1D ResNet algorithm is introduced in this study. The aim is to identify anomalous wafers by analyzing the raw time-series data collected from multiple sensors throughout the semiconductor manufacturing process. To achieve this objective, a set of features is chosen from specified tools in the process chain to characterize the status of the wafers. Tests on the available data confirm that the gradient vanishing problem faced by very deep networks starts to occur with the plain 1D Convolutional Neural Network (CNN)-based method when the size of the network is deeper than 11 layers. To address this, a 1D Residual Network (ResNet)-based method is used. The experimental results show that the proposed method works more effectively and accurately compared to techniques using a plain 1D CNN and can thus be used for detecting abnormal wafers in the semiconductor manufacturing industry.

What MEMS Research and Development Can Learn from a Production Environment

The intricate interdependency of device design and fabrication process complicates the development of microelectromechanical systems (MEMS). Commercial pressure has motivated industry to implement various tools and methods to overcome challenges and facilitate volume production. By now, these are only hesitantly being picked up and implemented in academic research. In this perspective, the applicability of these methods to research-focused MEMS development is investigated. It is found that even in the dynamics of a research endeavor, it is beneficial to adapt and apply tools and methods deduced from volume production. The key step is to change the perspective from fabricating devices to developing, maintaining and advancing the fabrication process. Tools and methods are introduced and discussed, using the development of magnetoelectric MEMS sensors within a collaborative research project as an illustrative example. This perspective provides both guidance to newcomers as well as inspiration to the well-versed experts.

Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data

In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment maintenance but also indicates potential line yield problems. Prompt AD based on available equipment sensory data (ESD) facilitates proactive yield and operations management. However, ESD items are highly diversified and drastically scale up along with the increased use of sensors. Even veteran engineers lack knowledge about ESD items for automated AD. This paper presents a novel Spectral and Time Autoencoder Learning for Anomaly Detection (STALAD) framework. The design consists of four innovations: (1) identification of cycle series and spectral transformation (CSST) from ESD, (2) unsupervised learning from CSST of ESD by exploiting Stacked AutoEncoders, (3) hypothesis test for AD based on the difference between the learned normal data and the tested sample data, (4) dynamic procedure control enabling periodic and parallel learning and testing. Applications to ESD of an HDP-CVD tool demonstrate that STALAD learns normality without engineers' prior knowledge, is tolerant to some abnormal data in training input, performs correct AD, and is efficient and adaptive for fab applications. Complementary to the current practice of using control wafer monitoring for AD, STALAD may facilitate early detection of equipment anomaly and assessment of impacts to process quality.

Devising Materials Manufacturing Toward Lab-to-Fab Translation of Flexible Electronics

Flexible electronics have witnessed exciting progress in academia over the past decade, but most of the research outcomes have yet to be translated into products or gain much market share. For mass production and commercialization, industrial adoption of newly developed functional materials and fabrication techniques is a prerequisite. However, due to the disparate features of academic laboratories and industrial plants, translating materials and manufacturing technologies from labs to fabs is notoriously difficult. Therefore, herein, key challenges in the materials manufacturing of flexible electronics are identified and discussed for its lab-to-fab translation, along the four stages in product manufacturing: design, materials supply, processing, and integration. Perspectives on industry-oriented strategies to overcome some of these obstacles are also proposed. Priorities for action are outlined, including standardization, iteration between basic and applied research, and adoption of smart manufacturing. With concerted efforts from academia and industry, flexible electronics will bring a bigger impact to society as promised.

Spatially Resolved Cross-Linking Characterization by Imaging Low-Coherence Interferometry

A method to characterize cross-linking differences in polymers such as waveguide polymers has been developed. The method is based on the scan-free information acquisition utilizing a low-coherence interferometer in conjunction with an imaging spectrometer. By the introduction of a novel analyzing algorithm, the recorded spectral-phase data was interpreted as wavelength-dependent optical thickness which is matchable with the refractive index and therefore with the degree of cross-linking. In the course of this work, the method was described in its hardware and algorithmic implementation as well as in its accuracy. Comparative measurements and error estimations showed an accuracy in the range of 10⁻⁶ in terms of the refractive index. Finally, photo-lithographically produced samples with laterally defined cross-linking differences have been characterized. It could be shown, that differences in the optical thickness of ±1.5 μm are distinguishable.

The effects of cleanroom noise intensity and frequency on physiological measures and subjective responses

BACKGROUND

Workplace noise exposure gains growing attention in high tech industry.

OBJECTIVE

This study investigated the noise effect on physiological and subjective responses in semiconductor manufacturing clean room environment.

METHODS

Twenty subjects including 10 males and 10 females completed all phases of the experiment. Each subject was asked to participate in four treatment combinations of two noise intensities [65 dB(A) and 80 dB(A)] × two frequency levels [high and low]. For each treatment condition, the subject was exposed to the specified noise condition in a sound proof cabin for one hour. The physiological measures included blood pressure and heart rate. The subjective measures included noise sensitivity, fatigue and annoyance.

RESULTS

The ANOVA results indicate that long-time noise exposure caused significant increase in blood pressure (p< 0.001). Furthermore, the noise intensity by time interaction effect was found to be significant on annoyance and fatigue.

CONCLUSIONS

The findings suggest that prolonged exposure to noise intensity at 80 dB(A) would result in a significant increase in physiological cost and subjective discomfort feeling. Thus, some countermeasures should be taken to reduce noise exposure and to promote health, and quality of working life.