An optimal design of semiconductor device and its process uniformity are critical factors affecting desired figure-of-merits as well as reducing fabrication cost of fixing possible malfunctioning in semiconductor manufacturing. Two main tasks in optimal device design for semiconductor manufacturing, i.e., parameter optimization and modeling, have been typically used either to characterize the devices by understanding how each parameter affects the device performances or to calibrate the parameters for SPICE circuit simulation. However, there still remains limitations in describing the relationship between all manufacturing parameters and figure-of-merits using several simple equations human experts can utilize. Even with the best model currently available, the optimal design of semiconductor device heavily relies on experiences of human experts and deals with time-consuming ad-hoc trials and non-holistic approaches. In this paper, we propose a new approach for data-based accurate electrical modeling of transistor, which is the most fundamental unit device of semiconductor, and fast optimization of its manufacturing parameters. Instead of the previous analytic approaches, finding finite equations derived from semiconductor physics, we utilize machine learning technique and neural networks to find appropriate modeling functions from data pairs of parameters and figure-of-merits. And for given desired figure-of-merits, we find optimal manufacturing parameters in holistic manner by using the learned functions of neural networks and fast gradient-based optimization method. Experimental results show that our neural-network-based-model directly estimate figure-of-merits with competitive accuracy and that our holistic optimization technique accurately and rapidly adapts the manufacturing parameters to meet desired figure-of-merits.

For decades, co-relating different data domains to attain the maximum potential of machines has driven research, especially in neural networks. Similarly, text and visual data (images and videos) are two distinct data domains with extensive research in the past. Recently, using natural language to process 2D or 3D images and videos with the immense power of neural nets has witnessed a promising future. Despite the diverse range of remarkable work in this field, notably in the past few years, rapid improvements have also solved future challenges for researchers. Moreover, the connection between these two domains is mainly subjected to GAN, thus limiting the horizons of this field. This review analyzes Text-to-Image (T2I) synthesis as a broader picture, Text-guided Visual-output (T2Vo), with the primary goal being to highlight the gaps by proposing a more comprehensive taxonomy. We broadly categorize text-guided visual output into three main divisions and meaningful subdivisions by critically examining an extensive body of literature from top-tier computer vision venues and closely related fields, such as machine learning and human–computer interaction, aiming at state-of-the-art models with a comparative analysis. This study successively follows previous surveys on T2I, adding value by analogously evaluating the diverse range of existing methods, including different generative models, several types of visual output, critical examination of various approaches, and highlighting the shortcomings, suggesting the future direction of research.

In this paper, by using neural network, we proposed a method to optimize Fully-Depleted (FD) Silicon-on-Insulator (SOI) Field-Effect-Transistor (FET) structures to maximize the on/off current ratio for 14-nm node (70-nm Gate Pitch) System-on-Chip (SoC) and sequential 3-dimensional integrated circuit (3DIC). Using machine learning method, the neural network accurately predicted the electrical behaviors of 14-nm node FDSOI FETs. Also by using backpropagation and gradient descent method, the device structures were modified to improve on/off current ratios for high performance (HP), low operating power (LOP), and low stand-by power (LSTP) applications. These optimized structures were secured within the process range of conventional FDSOI FETs. Among the optimized parameters, drain-side spacer length (Lspd), source/drain junction gradient (Lsdj), and thickness of source/drain epi (Tsd) showed different behaviors for each application and thickness of buried oxide (Tbox) was maximal in optimization results. The detailed physical analysis was conducted to evaluate these parameters for each application. The neural network based optimization was powerful and efficient while saving time and cost in device design.

With the prevalence of degenerative diseases due to the increase in the aging population, we have encountered many spine-related disorders. Since the spine is a crucial part of the body, fast and accurate diagnosis is critically important. Generally, clinicians use X-ray images to diagnose the spine, but X-ray images are commonly occluded by the shadows of some bones, making it hard to identify the whole spine. Therefore, recently, various deep-learning-based spinal X-ray image analysis approaches have been proposed to help diagnose the spine. However, these approaches did not consider the characteristics of frequent occlusion in the X-ray image and the properties of the vertebra shape. Therefore, based on the X-ray image properties and vertebra shape, we present a novel landmark detection network specialized in lumbar X-ray images. The proposed network consists of two stages: The first step detects the centers of the lumbar vertebrae and the upper end plate of the first sacral vertebra (S1), and the second step detects the four corner points of each lumbar vertebra and two corner points of S1 from the image obtained in the first step. We used random spine cutout augmentation in the first step to robustify the network against the commonly obscured X-ray images. Furthermore, in the second step, we used CoordConv to make the network recognize the location distribution of landmarks and part affinity fields to understand the morphological features of the vertebrae, resulting in more accurate landmark detection. The proposed network was evaluated using 304 X-ray images, and it achieved 98.02% accuracy in center detection and 8.34% relative distance error in corner detection. This indicates that our network can detect spinal landmarks reliably enough to support radiologists in analyzing the lumbar X-ray images.

A machine learning (ML) method was used to optimize the trap distribution of the charge trap nitride (CTN) to simultaneously improve its performance/reliability (P/R) characteristics, which are tradeoffs in 3-D NAND flash memories. Using an artificial neural network (ANN), we modeled the relationship between trap distributions and P/R characteristics. The ANN was trained using a large experimentally-calibrated technology computer-aided design (TCAD) simulation dataset. The gradient descent method was adapted to optimize the trap distribution, achieving the best P/R characteristics based on the well-trained ANN. Eventually, we found the best trap profile distributed in both space and energy. In particular, the energetic trap distribution had a larger impact on the P/R characteristics than that of the spatial trap distribution. Furthermore, in terms of the P/R characteristics, it was generally preferable to increase all inputs of the energetic trap distribution. However, the acceptor-like trap energy level ( E_TA ) and its standard deviation (σ _EA ) caused a tradeoff between P/R characteristics; therefore, ML was used to determine their optimal points. The proposed ML method allows the optimization of trap distribution to obtain the best P/R characteristics rapidly and quantitatively. Our findings could be used as a guideline for determining the physical properties of CTN in 3-D NAND flash cells.