The simulation and design of electronic devices such as transistors is vital for the semiconductor industry. Conventionally, a device is intuitively designed and simulated using model equations, which is a time-consuming and expensive process. However, recent machine learning approaches provide an unprecedented opportunity to improve these tasks by training the underlying relationships between the device design and the specifications derived from the extensively accumulated simulation data. This study implements various machine learning approaches for the simulation acceleration and inverse-design problems of fin field-effect transistors. In comparison to traditional simulators, the proposed neural network model demonstrated almost equivalent results (R² = 0.99) and was more than 122,000 times faster in simulation. Moreover, the proposed inverse-design model successfully generated design parameters that satisfied the desired target specifications with high accuracies (R² = 0.96). Overall, the results demonstrated that the proposed machine learning models aided in achieving efficient solutions for the simulation and design problems pertaining to electronic devices. Thus, the proposed approach can be further extended to more complex devices and other vital processes in the semiconductor industry.

Purpose: This study clarifies the relationship between corporate social responsibility (CSR) news reports and firm value and identifies the mechanisms that constitute this relationship. Specifically, it identifies the roles of word of mouth (WOM) and traditional advertising in this relationship.Design/methodology/approach: The data set used for the analysis covers 77 firms in Korea from 2012 to 2015. The random-effects model is applied to verify three hypotheses. Using a three-step regression analysis and the Sobel test, this study reveals the roles of WOM and advertising expenditure in the relationship between CSR news reports and firm value.Findings: CSR news reports positively affect firm value, and this relationship is mediated by WOM and moderated by advertising expenditure. Notably, the positive effect of WOM on firm value is stronger for companies that spend less on advertising.Practical implications: The evidence implies that marketing managers could benefit from not only conducting CSR activities but also widely publicizing them. CSR news reports could help companies enhance public awareness of and interest in them.Originality/value: This is the first study to investigate the influence of CSR news reports using empirical data in Korea.

Non-quantitative data have a significant impact on the financial market as well as quantitative data. In this paper, we propose CNN model of stock price prediction using Korean natural language processing. In the case of Korean natural language processing research was not actively performed compared to English language. We converted Korean sentences into nouns and vectorized them using skip-grams to extract the characteristics of the words. Then, the vectorized word sentence was used as input data of the CNN model to predict the stock price after 5 days of trading day. Most models have more than 50% prediction accuracy for stock price up and down. The highest accuracy of the model was about 53%. Since the result is not considerable but meaningful, it shows the possibility of developing the stock price prediction model through Korean natural language processing in the future.

Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversarial Network (cGAN) that has been routinely used for conditional sample generation. By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model; moreover, the model is robust against noises since adversarial training is employed. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems, respectively. The proposed framework is applied to stock market data and an image classification task. As a result, the proposed framework shows superior estimation performance, especially on noisy data; moreover, it is demonstrated that the proposed framework can properly estimate the uncertainty of predictions.

Owing to the recent explosive expansion of deep learning, several challenging problems in a variety of fields have been handled by deep learning, yet deep learning methods have been limited in their application to the network estimation problem. While network estimation has a possibility to be a useful method in various domains, deep learning-based network estimation has a limitation in that the number of variables must be fixed and the estimation cannot be performed by convolutional layers. In this study, we propose a Generative Adversarial Network (GAN) based method, called Inverse Covariance Estimating GAN (ICEGAN), which can alleviate these limitations. In ICEGAN, the concepts in Cycle-Consistent Adversarial Networks are modified for the problem and employed to adopt gene expression data. Additionally, the Monte Carlo approach is used to address the fixed size in the network estimation process. Thus, sub-networks are sampled from the entire network and estimated by ICEGAN; then, the Monte Carlo approach reconstructs the entire network with the estimations. In the simulation study, ICEGAN demonstrated superior performances compared to conventional models and the ordinary GAN model in estimating networks. Specifically, ICEGAN outperformed an ordinary GAN by 85.9% on average when the models were evaluated using the area under curve. In addition, ICEGAN performed gene network estimation of breast cancer using a gene expression dataset. Consequently, ICEGAN demonstrated promising results, considering the deep learning-based network estimation and the proposed Monte Carlo approach for GAN models, both of which can be expanded to other domains.