电气工程代写|模拟电路代写analog circuit代考|EEW240A

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

电气工程代写|模拟电路代写analog circuit代考|Background

The semiconductor industry is one of the marvels of modern society. We are constantly being presented with novel machines that utilize the extraordinary progress the industry has kept up over some seven decades of intense development. It was around 1965 that one of the Intel founders, Gordon Moore, coined what has become known as Moore’s law stating that for every 2 years, the number of transistors in a given area is doubling. It is an exponential scaling law that has kept up until recently (Fig. 1.1).

This is an extraordinary development driven by consumer demand for higher and higher data processing. The advent of streaming where full movies can be viewed as they are downloaded to a particular device requires enormous data delivery rates. In the beginning of this industry epoch, a commercial integrated circuit had a few hundred transistors at most. Compare this to the latest tens of billions of devices in a modern central processing unit (CPU) integrated circuit.

One of the fundamental units one measures these devices with is the so-called transistor gate length. It is as presently known to the author at $3 \mathrm{~nm}$ scale with the latest so-called gate-all-around technology. A typical atom has perhaps a size scale of $0.1 \mathrm{~nm}$, just 30 times smaller. We are at the realm of quantum physics for these devices. This is not new. Quantum effects like tunneling, where an electron can appear at the other side of a barrier with a certain probability, has been a source of the so-called leakage current for more than a decade.

Imagine now a modern integrated circuit or chip in industry parlance with some billion devices on it. The key development step of these products is the maskmaking step. There might be some 50 masks needed for the latest technology, and on average such masks cost a few 100,000 US dollars each. This cost is a large portion of the design engineering cost, and the total cost of designing such chips can then be of the order tens of millions of US dollars. This is before mass production starts. If something is wrong with the design, the masks need to be remade. How can one be reasonably sure that such enormous chips will be working when it comes back from the first fabrication run so a costly mask production step is avoided? The answer lies in the use of simulators, both digital, for the core data processing, and analog ones for the interfaces to the outside world among other things.

电气工程代写|模拟电路代写analog circuit代考|The Arrival of Simulators

Using simulators to prove out electronic circuitry is an old idea. The earliest attempts can be found in the 1960 s where the US Department of Defense supported circuit simulation developments that were proprietary. The modern attempts to make simulators publicly available were started by researchers at the University of California at Berkeley, where the extraordinary vision by a handful of young professors and researchers has developed what became known as Simulation Program with Integrated Circuit Emphasis or SPICE. It was not without controversy in the beginning. A lot of contemporaries felt that simulators could not possibly capture the operations well and the effort was a waste of time. Instead the idea was to prototype the design using breadboards and discrete devices and then miniaturize on a chip. The Berkeley team persisted and it is now considered the original master code, and most simulators after this use many of the same features SPICE introduced to solve numerical problems. In fact the word spice has become a verb in that one often says of simulating a circuit as “spiceing” a circuit. Naturally many decades of innovation have produced a code that is quite a bit more complex than the first versions.

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MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。