This paper demonstrates the learning of the underlying device physics by
mapping device structure images to their corresponding Current-Voltage (IV)
characteristics using a novel framework based on variational autoencoders
(VAE). Since VAE is used, domain expertise is not required and the framework
can be quickly deployed on any new device and measurement. This is expected to
be useful in the compact modeling of novel devices when only device
cross-sectional images and electrical characteristics are available (e.g. novel
emerging memory). Technology Computer-Aided Design (TCAD) generated and
hand-drawn Metal-Oxide-Semiconductor (MOS) device images and noisy
drain-current-gate-voltage curves (IDVG) are used for the demonstration. The
framework is formed by stacking two VAEs (one for image manifold learning and
one for IDVG manifold learning) which communicate with each other through the
latent variables. Five independent variables with different strengths are used.
It is shown that it can perform inverse design (generate a design structure for
a given IDVG) and forward prediction (predict IDVG for a given structure image,
which can be used for compact modeling if the image is treated as device
parameters) successfully. Since manifold learning is used, the machine is shown
to be robust against noise in the inputs (i.e. using hand-drawn images and
noisy IDVG curves) and not confused by weak and irrelevant independent
variables.

本文探讨利用基于变分自编码器 (VAE) 的新型结构，通过将器件结构图映射到相应的电流 - 电压 (IV) 特征，来学习底层器件物理学。使用 TCAD 生成的和手绘的 MOS 器件图像以及有噪声的漏电流 - 门电压曲线 (IDVG) 进行实证演示，而且该方法可以用于紧凑建模。

使用变分自编码器进行设备图像 - IV 映射的逆向设计和正向预测

Device Image-IV Mapping using Variational Autoencoder for Inverse Design  and Forward Prediction

Visually exploring in a real-world 4D spatiotemporal space freely in VR has
been a long-term quest. The task is especially appealing when only a few or
even single RGB cameras are used for capturing the dynamic scene. To this end,
we present an efficient framework capable of fast reconstruction, compact
modeling, and streamable rendering. First, we propose to decompose the 4D
spatiotemporal space according to temporal characteristics. Points in the 4D
space are associated with probabilities of belonging to three categories:
static, deforming, and new areas. Each area is represented and regularized by a
separate neural field. Second, we propose a hybrid representations based
feature streaming scheme for efficiently modeling the neural fields. Our
approach, coined NeRFPlayer, is evaluated on dynamic scenes captured by single
hand-held cameras and multi-camera arrays, achieving comparable or superior
rendering performance in terms of quality and speed comparable to recent
state-of-the-art methods, achieving reconstruction in 10 seconds per frame and
interactive rendering.

提供了一个快速重建、紧凑建模和可流式渲染的高效框架 NeRFPlayer，适用于用单个 RGB 摄像头捕捉的动态场景，同时使用神经场来表示和规范化三个分类：静态、变形和新领域。