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Lecture

SRAM: Peripherals and Noise Margin

In course

EE-429: Fundamentals of VLSI design

The course introduces the fundamentals of digital integrated circuits and the technology aspects from a designers perspective. It focuses mostly on transistor level, but discusses also the extension to large digital semicustom designs. ...

Description

This lecture covers the operation of SRAM, focusing on peripherals like row decoders, column multiplexers, and sense amplifiers. It explains the design considerations for SRAM reliability, static noise margin, and the importance of pre-decoding in improving performance.

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Instructors (2)

Alexandre Sébastien Julien Levisse

Andreas Peter Burg

Andreas Burg was born in Munich, Germany, in 1975. He received his Dipl.-Ing. degree in 2000 from the Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland. He then joined the Integrated Systems Laboratory of ETH Zurich, from where he graduated with the Dr. sc. techn. degree in 2006. In 1998, he worked at Siemens Semiconductors, San Jose, CA. During his doctoral studies, he was an intern with Bell Labs Wireless Research for a total of one year. From 2006 to 2007, he held positions as postdoctoral researcher at the Integrated Systems Laboratory and at the Communication Theory Group of the ETH Zurich. In 2007 he co-founded Celestrius, an ETH-spinoff in the field of MIMO wireless communication, where he was responsible for the ASIC development as Director for VLSI. In January 2009, he joined ETH Zurich as SNF Assistant Professor and as head of the Signal Processing Circuits and Systems group at the Integrated Systems Laboratory. In January 2011, he became a Tenure Track Assistant Professor at the Ecole Polytechnique Federale de Lausanne (EPFL) where he is leading the Telecommunications Circuits Laboratory in the School of Engineering. In June 2018 he was promoted to the role of a Tenured Associate Professor. In 2000, Mr. Burg received the Willi Studer Award and the ETH Medal for his diploma and his diploma thesis, respectively. Mr. Burg was also awarded an ETH Medal for his Ph.D. dissertation in 2006. In 2008, he received a 4-years grant from the Swiss National Science Foundation (SNF) for an SNF Assistant Professorship. In his professional career, Mr. Burg was involved in the development of more than 25 ASICs. He is a member of the IEEE and of the European Association for Signal Processing (EURASIP). Research interests and expertise

Circuits and systems for telecommunications (wireless and wired)
Prototyping and silicon implementation of new communication technologies
Development of communication algorithms and optimization for hardware implementation
Low-power VLSI signal processing for communications and other applications
Digital integrated circuits
Circuits for image and video processing

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Related concepts (32)

Static random-access memory

Static random-access memory (static RAM or SRAM) is a type of random-access memory (RAM) that uses latching circuitry (flip-flop) to store each bit. SRAM is volatile memory; data is lost when power is removed. The term static differentiates SRAM from DRAM (dynamic random-access memory) — SRAM will hold its data permanently in the presence of power, while data in DRAM decays in seconds and thus must be periodically refreshed.

Amplifier

An amplifier, electronic amplifier or (informally) amp is an electronic device that can increase the magnitude of a signal (a time-varying voltage or current). It is a two-port electronic circuit that uses electric power from a power supply to increase the amplitude (magnitude of the voltage or current) of a signal applied to its input terminals, producing a proportionally greater amplitude signal at its output. The amount of amplification provided by an amplifier is measured by its gain: the ratio of output voltage, current, or power to input.

Operational amplifier

An operational amplifier (often op amp or opamp) is a DC-coupled high-gain electronic voltage amplifier with a differential input and, usually, a single-ended output. In this configuration, an op amp produces an output potential (relative to circuit ground) that is typically 100,000 times larger than the potential difference between its input terminals. The operational amplifier traces its origin and name to analog computers, where they were used to perform mathematical operations in linear, non-linear, and frequency-dependent circuits.

Transformer (machine learning model)

A transformer is a deep learning architecture that relies on the parallel multi-head attention mechanism. The modern transformer was proposed in the 2017 paper titled 'Attention Is All You Need' by Ashish Vaswani et al., Google Brain team. It is notable for requiring less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl, by virtue of the parallelized processing of input sequence.

Noise (signal processing)

In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise. Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters.

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