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1 Basic MOS Device Physics. 2 Why Analog Circuits? DSP algorithms were predicted to replace all analog blocks with the flexibility in silicon implementation.

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Presentation on theme: "1 Basic MOS Device Physics. 2 Why Analog Circuits? DSP algorithms were predicted to replace all analog blocks with the flexibility in silicon implementation."— Presentation transcript:

1 1 Basic MOS Device Physics

2 2 Why Analog Circuits? DSP algorithms were predicted to replace all analog blocks with the flexibility in silicon implementation Analog circuits are still required in today’s  Complex  high-performance systems Natural signals are analog in nature  seismographic sensor – voltage ranging from microvolts to milivolts;  microphone – pick up sound of an orchestra in microvolts – milivolts range; These natural signals will eventually undergo processing in digital domain  hence the need for an analog-to-digital converter and a DSP

3 3 Analog? Electrical version of natural signals too small for digitization by ADC  Coupled with unwanted, out-of-band noise  Hence the need to amplify the small signal + filter out the unwanted noise  High-performance ADC, filter & amplifier -> hot research topics

4 4 Digital Communications Let’s look at some digital examples that justify the need for analog circuits today Binary data often transmitted over long distances High-speed data travelling through long-distance  Signal experiences attenuation and distortion Does not resemble the original digital waveform How to recover the original signal? A receiver with amplifier + filter + ADC? Data recovery circuit (base: PLL)

5 5 Digital Communication What if instead of binary signals, we use “multi- level” signals  e.g. every 2 consecutive bits in the sequence are grouped and converted to one of four levels Each level is twice as long as a bit period As a result, only require half the bandwidth Improves quality of communication  Use of multi-level -> a DAC is required to produce multiple levels from the grouped binary data Increase number of levels -> relaxes bandwidth requirements  At the same time, requires high precision DAC and ADC

6 6 Disk Drive Electronics Data stored on hard disk in digital form When data is read by magnetic head  Converted to electrical signal Amplitude in milivolts High noise content Substantial distortion  To recover the signal Need to amplify, filter, digitize then go through further processing Design of each block is challenging -> high-speed and high-performance requirement

7 7 Wireless Communications Signal picked up by antenna of a RF receiver  Microvolts amplitude with center freq in GHz range  Accompanied by large interferers Recovery:  Amplify low-level signal with min noise (LNA) at high-freq Trade-offs: noise, operating freq, tolerance of interferers, power, cost

8 8 Optical Receivers Long distance high-speed signal transmission  Can’t use cables -> limited BW, high attenuation Optical approach:  Transmitter end convert data to light (use laser diode), transmit over optical fiber (wide band, very low loss)  Receiver end Convert light to small electrical current by a photodiode Process the low-level signal at high-speed (rqmt: low- noise, broadband cct design)

9 9 Why is analog design challenging? Digital circuits deal primarily with speed power tradeoff. Analog circuits deal with multi-dimensional tradeoff of speed, power, gain, precision, supply, … Due to speed and precision requirements, analog circuits are much more sensitive to noise, crosstalk, and other interferers.

10 10 Why is analog design challenging? (cont.) Analog circuits are much more sensitive to second-order device effects High performance analog circuit design can rarely be automated - typically require hand-crafted design and layout Modeling and simulation of analog circuits is still problematic, requiring experience and intution

11 11 Why is analog design challenging? (cont.) Economic forces require the development of analog circuits in mainstream digital processes Economic forces pushing the integration of analog and digital functions onto a single substrate Many levels of abstraction are required

12 12 Flow for this course


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