EE 230 Lecture 44 Spring 2010

EE 230 Lecture 44 Spring 2010 - EE 230 Lecture 44 Data...

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EE 230 Lecture 44 Data Converters Nonideal Effects
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Characterization of Nonlinearities Linearity metrics: INL DNL THD SFDR IN LK OUT IN LK OUT Linearity Metrics for ADC and DAC are Analogous to Each Other Review from Last Time:
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Integral Nonlinearity (INL) Linearity metrics: INL DNL THD SFDR ( ) ( ) kT R A N F I T INL =X k -X k LSB LSBF INL INL X = n LSB REF INL INL 2 X { } 1 k INL max INL kN ≤≤ = INL of an ideal ADC is 0 Review from Last Time:
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Differential Nonlinearity (DNL) Linearity metrics: INL DNL THD SFDR () k TRANS TRANS LSB DNL X (k)-X k-1 -X { } 1 k DNL max DNL kN <≤ = Review from Last Time:
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Equivalent Number of Bits -ENOB (based upon linearity) 10 10 1 2 log ENOB = n- log ν If ν is the INL in LSB 0.5 n 1n - 1 2n - 2 4n - 3 8n - 4 16 n-5 ν res Review from Last Time:
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Spectral Characterization Linearity metrics: INL DNL THD SFDR INL and DNL do not give a good indicator of linearity of a data converter in some (many) applications THD and SFDR are alternate ways to characterize the linearity of a data converter Review from Last Time:
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Spectral Characterization ( ) IN M X=Xs in ω t+ θ ( )( ) 2 OUT 0 1 1 X= A + A s i n ω t+ θ +A s i n k ω t+ θ kk k γ = ++ A k , k>1 are all spectral distortion components Generally only first few terms are large enough to represent significant distortion 2 2 2 1 k k A THD A = = 2 2 10 2 1 10 log k k dB A THD A = ⎛⎞ ⎜⎟ = ⎝⎠ {} 1 k 1<k A SFDR= max A 1 dB 10 k 1<k A SFDR =20log Review from Last Time:
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Spectral Characterization Key theorem useful for spectral characterization ( ) IN M X=Xs in ω t+ θ ( )( ) 2 OUT 0 1 1 X= A + A s i n ω t+ θ +A s
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EE 230 Lecture 44 Spring 2010 - EE 230 Lecture 44 Data...

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