在互補式金氧半(CMOS)積體電路中，隨著量產製程的演進，元件的尺寸已縮減到深次微 米(deep-submicron)階段，以增進積體電路(IC)的性能及運算速度，以及降低每顆晶片的製造 成本。但隨著元件尺寸的縮減，卻出現一些可靠度的問題。 在次微米技術中，為了克服所謂熱載子(Hot-Carrier)問題而發展出 LDD(Lightly-Doped Drain) 製程與結構; 為了降低 CMOS 元件汲極(drain)與源極(source)的寄生電阻(sheet resistance) Rs 與 Rd，而發展出 Silicide 製程; 為了降低 CMOS 元件閘級的寄生電阻 Rg，而發展出 Polycide 製 程 ; 在更進步的製程中把 Silicide 與 Polycide 一起製造，而發展出所謂 Salicide 製程
标签： Protection CMOS ESD ICs in
上传时间： 2020-06-05
上传用户：shancjb
在互補式金氧半(CMOS)積體電路中，隨著量產製程 的演進，元件的尺寸已縮減到深次微米(deep-submicron)階 段，以增進積體電路(IC)的性能及運算速度，以及降低每 顆晶片的製造成本。但隨著元件尺寸的縮減，卻出現一些 可靠度的問題。
标签： ESD_Technology
上传时间： 2020-06-05
上传用户：shancjb
ESD is a crucial factor for integrated circuits and influences their quality and reliability. Today increasingly sensitive processes with deep sub micron structures are developed. The integration of more and more functionality on a single chip and saving of chip area is required. Integrated circuits become more susceptible to ESD/EOS related damages. However, the requirements on ESD robustness especially for automotive applications are increasing. ESD failures are very often the reason for redesigns. Much research has been conducted by semiconductor manufacturers on ESD robust design.
标签： Guidelines Design Basic ESD
上传时间： 2020-06-05
上传用户：shancjb
Signals convey information. Systems transform signals. This book introduces the mathe- matical models used to design and understand both. It is intended for students interested in developing a deep understanding of how to digitally create and manipulate signals to measure and control the physical world and to enhance human experience and communi- cation.
标签： Interpretation Structure and Signals Systems of
上传时间： 2020-06-09
上传用户：shancjb
The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：shancjb
Inventors have long dreamed of creating machines that think. This desire dates back to at least the time of ancient Greece. The mythical figures Pygmalion, Daedalus, and Hephaestus may all be interpreted as legendary inventors, and Galatea, Talos, and Pandora may all be regarded as artificial life ( , Ovid and Martin 2004 Sparkes 1996 Tandy 1997 ; , ; , ).
上传时间： 2020-06-10
上传用户：shancjb
We’re living through exciting times. The landscape of what computers can do is changing by the week. Tasks that only a few years ago were thought to require higher cognition are getting solved by machines at near-superhuman levels of per- formance. Tasks such as describing a photographic image with a sentence in idiom- atic English, playing complex strategy game, and diagnosing a tumor from a radiological scan are all approachable now by a computer. Even more impressively, computers acquire the ability to solve such tasks through examples, rather than human-encoded of handcrafted rules.
标签： deep-Learning-with-PyTorch
上传时间： 2020-06-10
上传用户：shancjb
Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
标签： Embedded_deep_Learning Algorithms
上传时间： 2020-06-10
上传用户：shancjb
上面是一段实时目标识别的演示， 计算机在视频流上标注出物体的类别， 包括人、汽车、自行车、狗、背包、领带、椅子等。今天的计算机视觉技术已经可以在图片、视频中识别出大量类别的物体， 甚至可以初步理解图片或者视频中的内容， 在这方面，人工智能已经达到了3 岁儿童的智力水平。这是一个很了不起的成就， 毕竟人工智能用了几十年的时间， 就走完了人类几十万年的进化之路，并且还在加速发展。道路总是曲折的， 也是有迹可循的。在尝试了其它方法之后， 计算机视觉在仿生学里找到了正确的道路（至少目前看是正确的） 。通过研究人类的视觉原理，计算机利用深度神经网络（ deep Neural Network，NN）实现了对图片的识别，包括文字识别、物体分类、图像理解等。在这个过程中，神经元和神经网络模型、大数据技术的发展，以及处理器（尤其是GPU）强大的算力，给人工智能技术的发展提供了很大的支持。本文是一篇学习笔记， 以深度优先的思路， 记录了对深度学习（deep Learning）的简单梳理，主要针对计算机视觉应用领域。
上传时间： 2022-06-22
上传用户：lw125849842
Introduction The Sil9135/Sil9135A HDMI Receiver with Enhanced Audio and deep Color Outputs is a second-generation dual-input High Definition Multimedia Interface(HDMI)receiver. It is software-compatible with the Sil9133receiver, but adds audio support for DTS-HD and Dolby TrueHD. Digital televisions that can display 10-or 12-bit color depth can now provide the highest quality protected digital audio and video over a single cable. The Sil9135and Sil9135A devices, which are functionally identical, can receive deep Color video up to 12-bit,1080p @60Hz. Backward compatibility with the DVI 1.0specification allows HDMI systems to connect to existing DVI 1.0 hosts, such as HD set-top boxes and PCs. Silicon Image HDMI receivers use the latest generation Transition Minimized Differential Signaling(TMDS) core technology that runs at 25-225 MHz.The chip comes pre-programmed with High-bandwidth?
上传时间： 2022-06-25
上传用户：ibeikeleilei
