It took me a few months to finally read this book, but it was well worth it. I have been reading it prior to sleep as it was so full of information that it was difficult to read more than ten pages without taking a break to think about all of the new ideas. Furthermore, the information was presented in such an accessible manner that even those who are not specialists in relativity, topology or physics can appreciate the message.
I selected this book because I figured the topic was far away from electrical engineering that it could give a new perspective on understanding what is implied by measuring time and distance. Sure enough, this book provided many insights into the nature of our universe through the relation of time and space measurement. I will avoid summarizing the book, however, I will mention that it would be a pleasant read for those interested in non-Eucledian coordinates and the effects of gravitational fields. The book is extremely well written and reads much like a lecture series where the audience does not need to be able to carry out all of the steps of each operation, but acquires a taste for the process and a deeper appreceation. From the point of view of technical written English, this was one of the most understandable books on a physical subject that I have read in some time.
When looking at the application notes section of Fujitsu’s site, I came across their FRAM memory guide book. I was surprised as I did not know what FRAM really was and so I flipped throug. Basically, a film deposition process was developed, which is compatible with standard CMOS processing, that introduces films who can maintain their polarization after the applied electric field is removed. We are all familiar with ferromagnetic devices, these are the pieces of metal that can be magnetized when placed in a constant magnetic field. Thanks to some nice electromagnetic research, we can do something similar with thin films and thereby create ferroelectric capacitors that are capable of retaining data without applied power while being as fast as SDRAM. It is clear that half of the Fujitsu guide is a sales pitch for their ICs, however, the other good is a fairly good introduction to the FRAM technologies. The basic technology is discussed along with some typical ferroelectric substrates. A reference list is also attached.
According to Look Around You, an investigative scientific program appearing on the BBC’s Channel 3, a new atomic element that may revolutionize semiconductor fabrication has been successfully formulated in laboratory conditions. This element is Intelligent Calcium (see above) which may replace sodium ion implantation in the near future and thereby increase both digital and analog circuit performance.
From a design standpoint, ion implantation is one of the crucial steps in integrated circuit manufacturing as it allow the designer some freedom to set the threshold voltage for a MOSFET transistor as well as negate some of the potential problems with manufacturing. The basic idea is that by applying a positive or negative voltage at the gate terminal, we can attract either negative or positive charges (pairs of which are constantly thermally generated) to the “top” of the device respectively. If enough of these charges accumulate, we can form a conducting channel through the substrate. By implanting immobile ions in the gate oxide region, we can change the voltage at which this channel formation begins to occur and thereby the required bias for transistor operation. It is not hard to imagine that some chemical process steps may add undesired ions at the silicon-oxide interfaces in addition to dangling bonds in the oxide, so this same technique may sometimes be used to balance the parasitic ion concentration due to processing and return the device to the designed activation threshold.
Typically, the positive ion of choice is sodium. Ions are generated by electrically heated metal and are then accelerated by electromagnetic fields until the impact the target. Upon impacting the crystal lattice, the sodium looses momentum and typically does not move from its resting position unless the device is severely heated (can happen!). The sodium’s only action is to interact with the charges around it and modulate the effective threshold voltage for the device. The main downside is that the sodium ion cannot ‘decide’ when to act, so its effects are constant throughout time.
This is where the concept of intelligent calcium comes in. Unlike the ‘dumb’ sodium, the intelligent calcium’s higher atomic weight allows it higher flexibility with its charge configuration and thereby more freedom to ‘decide’ when to act as a 2+ valence ion and when to pretend to be neutrally charged. By using intelligent calcium as a positive ion throughout an integrated circuit, a calcium network is formed where each atom becomes a node and can communicate with both adjacent and far-away atoms to get a general feel for the situation and the activity of the device. It can then modulate its charge to increase (or decrease) the individual transistor thresholds as needed. From an analog perspective, the transconductance of the device goes up tremendously as well as the frequency response (due to intelligent calcium’s rapid activation). From a digital perspective, the speed of information propagation in the intelligent calcium network exceeds the mobilities of both holes and electrons, even in a strained silicon lattice. For this reason, the transistors adjust their threshold in advance of the gate voltage changes and thereby increase their switching speeds. This in turn translates to quicker gates and overall quicker devices.
The future is bright for intelligent calcium as it has many desirable properties for semiconductor fabrication. Scientists are presently pushing the bleeding edge of technology as they investigate the possibility of using the intelligent calcium network as a means to communication between transistors and a total replacement for the metal interconnects. The progress is slow, however, I have full confidence that I will one day have the opportunity to image an metal-less, intelligent calcium powered device in the weekly IC Friday column.
I randomly found this book in the in the sciences section of a local book store and decided to buy it given that it was only about ten dollars. I was pleasantly surprised with the depth of the book, the readability, and the commentary of the author on applying the latest understanding of our physical world to the long-standing philosophical questions such as those dealing with determination and free will.
The book starts out by giving a summary statement of physical and philosophical advancements to year of the first publication (1943). For me, the historical accounts of philosophical advancement was very interesting given my ignorance of the subject. I was surprised to learn that Gottfried Leibniz, one of the fathers of calculus, had also tried to create a operational logic system to make philosophy universal and on par with mathematical proofs. Unfortunately he failed and to this day this universal language is still missing. The physical overview spends substantial time on new discoveries in quantum mechanics and goes over the probabilistic nature of the universe and ties this together with philosophical understanding of reality, knowledge and will. I will not spoil the conclusions, however, I will mention that they are well stated and supported.
Besides the substantial new (to me) content of this book, I found the book to be easily readable and understandable. The book was very complete and even offered references for further study of various intricate subjects. The organization of the book superb where each chapter built on information from the previous chapters and everything flowed together. I highly recommend it does a good job of provoking critical thought and introducing the reader to questions regarding our position and some of the motivations for the way we carry out our lives.
ISBN 0-484-24117-3 (This is the ISBN number off the back of the book, however, I noticed that this book is a little hard to find. A title and author search might work better than the ISBN.)
When trying to do parameter estimation given a set of data, there are typically two approaches: least squares estimation and maximum likelihood estimation. In both cases, a model must be constructed where the first case tries to fit the data to the model by minimizing residual errors while the second method tries to estimate the probability density function associated with the collected data and thereby determine the parameters. While trying to make sense of things, I found this tutorial on maximum likelihood estimation by In Jae Myung of Ohio State University to be very helpful as it provided a description as well as MATLAB code examples. (He also also publishes a list of books currently on his bookshelf!)
Update:Here is a link to a paper outlining R.A. Fishers arrival at the concept of maximum likelihood. An interesting thing to note here is that given a likelihood function P, log(P) is often maximized, yielding maximum likelihood because the function would have to be differentiated and given that the probability distribution of many naturally occurring events is Gaussian, differentiating the logarithm of such a probability density function just makes more sense.
While looking up some papers on statistics I came across this freely-available copy of William Gosset’s March 1908 paper, almost a year before the its United States copyright expires and it becomes public domain. The paper is a very nice transcription of the original paper (available through JSTOR) which makes it much easier to read.
The point of the paper relates a statistical confidence limit and a set of bounds within which the mean of a set of measurements must lie. To be more specific, the paper addresses measurements whose distribution is Gaussian and goes on to specify how small a error bound you can place around the mean given a finite number of experiments with a finite certainty, say 95%. Furthermore, the usefulness of the paper comes from the fact that the limit is examined with a decreasing number of observations. This is useful since certain experiments cannot be performed many times and we need to be able to say how certain we are of the mean of the observed Gaussian random variable given the finite number of data points.
In a small side note, the p < 0.05 (or >95% “certainty”) is often considered to be a “good” value, but it may seem somewhat arbitrary. There are some people who attribute this to Karl Fisher, more specifically, his publication titled Statistical Tables for Agricultural, Biological and Medical Research (3.3MB). For those looking for Fisher’s historical papers, they can be found at the University of Adelaide.
This is a continuation of the learning for free on the internet series, this time with reading. I have had the misfortune of having to study quantum mechanics on a graduate level, after getting an undergraduate in computer engineering, from Introductory Quantum Mechanics by Liboff (Amazon). This book was tedious to say the least and having to study from it to pass a qualifier was even worse. Fortunately, there are people, such as Doron Cohen, who can write coherently and choose to publish their lectures on quantum mechanics on line. Although it may seem that quantum mechanics is not important to the engineer, one can remember that a bipolar-junction transistor cannot be properly described without a little bit of quantum mechanics. Many designs that push performance limits will also find themselves involved with the laws of quantum mechanics. Lastly, when the laws that govern quantum mechanics are extrapolated to the classical world, the result of an argument can become a superposition of states and otherwise reasonable people can be driven to madness.
Design Downloaded from WPThemes.Info Copyrights for all of the original writings and ideas belong to Nick Chernyy, use them as you like but give credit where it is due.
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