Restructuring TCAD System: Teaching Traditional TCAD New Tricks

Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.Comment: In Proceedings of 2021 IEEE International Electron Devices Meeting (IEDM

PAC-Net: A Model Pruning Approach to Inductive Transfer Learning

Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization with the pre-trained model and fine-tuning for the target task. However, when using over-parameterized models, we can often prune the model without sacrificing the accuracy of the source task. This motivates us to adopt model pruning for transfer learning with deep learning models. In this paper, we propose PAC-Net, a simple yet effective approach for transfer learning based on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate (PAC). The main idea behind these steps is to identify essential weights for the source task, fine-tune on the source task by updating the essential weights, and then calibrate on the target task by updating the remaining redundant weights. Under the various and extensive set of inductive transfer learning experiments, we show that our method achieves state-of-the-art performance by a large margin

Microfluidic Detection and Analysis of Microplastics Using Surface Nanodroplets

Detection of microplastics from water is crucial for various reasons, such as food safety monitoring, monitoring of the fate and transport of microplastics, and development of preventive measures for their occurrence. Currently, microplastics are detected by isolating them using filtration, separation by centrifugation, or membrane filtration, subsequently followed by analysis using well-established analytical methods, such as Raman spectroscopy. However, due to their variability in shape, color, size, and density, isolation using the conventional methods mentioned above is cumbersome and time-consuming. In this work, we show a surface-nanodroplet-decorated microfluidic device for isolation and analysis of small microplastics (diameter of 10 μm) from water. Surface nanodroplets are able to capture nearby microplastics as water flows through the microfluidic device. Using a model microplastic solution, we show that microplastics of various sizes and types can be captured and visualized by using optical and fluorescence microscopy. More importantly, as the surface nanodroplets are pinned on the microfluidic channel, the captured microplastics can also be analyzed using a Raman spectroscope, which enables both physical (i.e., size and shape) and chemical (i.e., type) characterization of microplastics at a single-particle level. The technique shown here can be used as a simple, fast, and economical detection method for small microplastics

Bridging TCAD and AI: Its Application to Semiconductor Design

There is a growing consensus that the physics-based model needs to be coupled with machine learning (ML) model relying on data or vice versa in order to fully exploit their combined strengths to address scientific or engineering problems that cannot be solved separately. We propose several methodologies of bridging technology computer-aided design (TCAD) simulation and artificial intelligence (AI) with its application to the tasks for which traditional TCAD faces challenges in terms of simulation runtime, coverage, and so on. AI-emulator that learns fine-grained information from rigorous TCAD enables simulation of process technologies and device in real-time as well as large-scale simulation such as full-pattern analysis of stress without high demand on computational resource. To accelerate atomistic molecular dynamics (MD) simulation, we have done a comparison study of descriptor-based and graph-based neural net potential, and also show their capability with large-scale and long-time simulation of silicon oxidation. Finally, we discuss the use of hybrid modeling of AI- and physics-based model for the case where physical equations are either fully or partially unknown

Real-time TCAD: A new paradigm for TCAD in the artificial intelligence era

This paper presents a novel approach to enable real-time device simulation and optimization. State-of-the-art algorithms which can describe semiconductor domain are adopted to train deep learning models whose input and output are process condition and doping profile / electrical characteristic, respectively. Our framework enables to update automatically deep learning models by estimating the uncertainty of the model prediction. Our Real-Time TCAD framework is validated on 130nm processes for display driver integration circuit (DDI), and 1) prediction time was 530, 000 times faster than conventional TCAD, and time spent for process optimization was reduced by 300, 000 times compared to human expert, 2) the model achieved average accuracy of 99% compared to TCAD simulation results, and thus, 3) process development time for DDI was reduced by 8 weeks