Active inductor load can be designed by applying a negative voltage feedback from drain of an active current load to the gate of the active load. The feedback has a time constant that depends on the size of gate resistor and Cgs of the active load. Overall, feedback makes the load to behave like an inductor where $L=\frac{R_g C_{gs}}{g_m}$. For more details refer to page 68 of this Master's thesis. The following image is also captured from the thesis.
Wednesday, March 30, 2016
Tuesday, March 29, 2016
Spectre Simulation (Fundamental)
"Simulation of Analog and Mixed-Signal Circuits by Ken Kundert" slides are a quick review of the book, "The Designer's Guide to SPICE and Spectre". The book goes through the details of spice simulation and explains the trad-offs.
CMOS chopper amplifier to reduce 1/f noise
The paper reviews CMOS chopper amplifier theory and actual circuit. The theory is simple: modulate the input signal to F_chopper where 1/f noise is not significant, amplify the modulated signal. and finally demodulate and reconstruct low pass signal at the output. Also, found this work interesting: using CMOS devices in lateral bipolar mode to decrease 1/f noise (see figure 14).
Sunday, December 13, 2015
Wednesday, December 9, 2015
Voltage Mode R-2R DAC: Theory of Operation
This document is about "how voltage mode R-2R DAC does work?". A major goal is to extract the math behind R-2R DAC intuitive structure.
Friday, August 7, 2015
Random DC Offset of Comparators
These slides review Flash ADC circuits. The source of random DC offset is the random fabrication mismatch. The reference paper, "Matching Properties of CMOS Transistors" (1989) divides the mismatch to two models: local and global.
Wednesday, June 3, 2015
Charge Injection in CMOS switches
"Charge Injection in Analog CMOS Switches" (1987) presents a model for charge injection. It also proposes some methods to alleviate charge injection by design.
Wednesday, May 13, 2015
Tuesday, May 12, 2015
Fractional Frequency Divider by Delta-Sigma Modulator
"Delta-sigma modulation in fractional-N frequency synthesis" (1993) compares pulse swallowing, phase interpolation, and Wheatley random jittering methods with Delta-Sigma jittering technique.
Wednesday, April 1, 2015
CMOS Capacitance and Delay
handy notes on CMOS delay. Analyse the capacitance and connect it to delay of circuits (driver load and total load capacitance).
Saturday, November 1, 2014
Saturday, October 18, 2014
A tutorial on Phase Locked Loops
This tutorial is recommended by a senior member of my team: "Design of Monolithic Phase Locked Loops and Clock Recovery Circuits: A Tutorial".
Saturday, July 12, 2014
Are uncorrelated normal random variables necessarily independent?
Professor Rosenthal nicely explained this questions by giving two clear examples. The golden quote is "What is true is that if the random variable pair (X,Y) follows the bivariate normal distribution, and Cov(X,Y) = 0, then X and Y must be independent. But what is not true is that if each of X and Y is normally distributed, and Cov(X,Y) = 0, then X and Y must be independent".
Saturday, May 17, 2014
An Overview of Boundary Scan Test Methodology
An abstract review of boundary scan test methodology is available here. Pros and cons are introduced as below:
Benefits:
Benefits:
- Reusable Test Vectors
- Reduced Test Time
- Reduced Time to Market
- Faster ROI
- Reduced Design Iterations
- Efficient and Economical Production
- Functional Test
Challenges:
- Area Overhead / Additional Circuits
- Additional Pins
- Higher Design Effort
- Performance Degradation
- Power Consumption
Friday, April 18, 2014
Digital Signatures Verification
Heartbleed bug and stories around it motivated me to review the SSL security. Here is a good review of Digital Signature. It provides graphical flow charts of the procedure that eases the review ....
Wednesday, February 12, 2014
Multivariate Mutual Information
Here is a useful review on "Multivariate Mutual Information". I'm working on the negative interaction in a tri-variate problem. The definition of semi-independent distribution caught my attraction. From [Han'80: Multiple Mutual Informations and Multiple Interactions in Frequency Data], a tri-variate distribution (U,V,Y) is semi-independent if
Pr{U V Y} = Pr{U} Pr{V Y} + Pr{V} Pr{U Y} + Pr{Y} P r{U V} − 2 Pr{U} Pr{V} Pr{Y}
Pr{U V Y} = Pr{U} Pr{V Y} + Pr{V} Pr{U Y} + Pr{Y} P r{U V} − 2 Pr{U} Pr{V} Pr{Y}
Friday, November 22, 2013
DFT and windowing
Recently, I was working on a feasibility study of measuring/detecting IM3s of an ADC output by capturing only 512 samples, where the sampling rate is about 300MS/sec. During this research, I came across "On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform", by F. Harris (1978). This paper is a must-read for signal processing folks.
The concepts such as spectral resolution, the window processing loss (gain), 6-dB BW of the window, etc. need to be considered in any DFT design/analysis. As the figure below, with 512 samples, the signal intermods are not detectable if we do not use a proper window.
The concepts such as spectral resolution, the window processing loss (gain), 6-dB BW of the window, etc. need to be considered in any DFT design/analysis. As the figure below, with 512 samples, the signal intermods are not detectable if we do not use a proper window.
Wednesday, October 30, 2013
Gilbert Mixer: Secod order order (IM2/HD2) nonlinearities
In one of our tests, we noticed a significant HD2 at the output of a differential passive Gilbert cell mixer (the mixer performance was supposed to be at least 20dB better than what we measured in the lab). I was looking for a systematic methodology to link this problem to load mismatches on P and N paths. In my research, I came across this paper [this webpage includes better quality images] that categorizes second order nonlinearities of a Gilbert cell. My take on this work is Eq. (16), where the IM2 output voltage is extracted by i_im2_diff (differential current IM2) and i_im2_cm (common mode current IM2). The equation express the relation between the overall IM2 and internally+externally generated IM2. In other words, if the output load is matched (load of path N and P are equal in terms of phase and amplitude) then i_im2_cm's impact will be canceled by teh balance between the loads (P&N) , i.e. delta_Rload*i_im2_cm becomes almost 0. On the other hand, i_im2_diff (generated by internal transconductance/timing mismatches) will be signified by sum_Rload (Rload_P+R_load_N). In my case, I guessed that the strong HD2 would have been generated by P&N load mismatched; however, later we found out that the input signal to the mixer was imbalance!!!
Tuesday, August 27, 2013
Wednesday, July 31, 2013
On oversampling of quantization noise
This is about Eq.(3) of [Candy'92]. Basically, I keep asking myself why the sampled quantized noise spectral density is given by

where the quantization noise is a white process with the following RMS value:

[Candy'92] explains that due to sampling, the noise power folds into the frequency band
However, I have to find a self-convincing explanation.
Here is how I understand the impact of oversampling on quantization noise:
1- we should consider a LPF with the bandwidth of

2- the sampling/quantization system is as follows [x denotes the instantaneous quantization noise]:
x--|sampler|--|LPF: h[n]|--y
![E\left[ x^2\left[n_1\right] \right ] = e_{\text{rms}}^2](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_uOu6pE705kdazO2tL41ycmG3N0tGnjPBvCCsEZhiYh3T3wsia0nSq5EqrBr0YbpLfBQ09eRPlOi5t7xN-KDVjiWn29bSESg2x6oopxJjoKiTFPlrp-5y2MC6QGG7IUwqoX2LxBavo95eJV0JMW_363R9W9MmVPFy9Ne_Hg7LgUGZb5mpilKyqo1Z0EoZGLQSv9NBW643H-XlCY5K_oc6_s8fHfFQCYchQnbnNBTBaoV1IPXUgDuMsNz24=s0-d)
3- Auto-covariance of LPF output is given by:
![\begin{align*} \text{E} \left[ y \left[ n_1 \right] y\left[ n_1\right] \right] &= \sum_{m=-\infty}^{+\infty}\sum_{k=-\infty}^{+\infty} \text{E} \left[x\left[n_1 \right] x\left[n_1 \right] \right] h \left[ n_1 -m \right] h \left[ n_1 -k \right] \\ &= \text{e}_{\text{rms}}^2 \sum_{m=-\infty}^{+\infty}\sum_{k=-\infty}^{+\infty} h \left[ n_1 -m \right] h \left[ n_1 -k \right] \\ &= \text{e}_{\text{rms}}^2 \left(\sum_{m=-\infty}^{+\infty} h \left[ n_1 -m \right] \right)^2 \end{align*}](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_sfjpcuW_KppaoabvIVq5nkT36rrNLB-qQ8vSvzmHXKOcfp1kgcDE7sJSHQ-2rUpOpimFZO5XU2U3dBaSa23F3P3k-gYC1uE4JUGRVTMQ1ASaQ62o-I4wPK08OUfjvmFgWcqQeAV04IJ5IBOaJGyEsWkyjrWFi-2pTPgXt1uy_N4061kDwIjXQMMq0QLU5PeBQ_WNUJ1BtnsJIGcgG0-wT6OuvfTjtEW5SWaYCaHrNcpns1Ng7Qqtt2ejiUzKW8elEMRCBdBp19DPCkQxZ2e-qSSgRwVAdSUz5Ck8LKuTXdNsmW2BrKfQ70RjJgLRgO9RyZwgukH1eDkv-hNzkvk5lm8dfNz1PKT2YtScbLIXcjcEwdSd23vP5xtxBjc9OLVv6VK_VjMP6du7DPiBh2biwQDV-48aiQs_NUbe6A5AsseCG1LpT82x2rV2_gTJkkCap-J5LlqQNg7jPY0ZQuN3_77PwTkyhrB_w9Cria2lExktNZvWwJzB9IEkFjixyG-QfyL9ZiiJZV7Vs8U8xpGxqqa5BhKDY7osiFPGPRB-I3DLH1_vLI2TYresv8-Q0OQQsrDIBmms5_Ufi3OsAMqCaTsQDj1i9aZWPOV-kJKIb7VEDJAa3y6GAd1ZIeqw2RNXRAhwD-Hb44fg6-A9eR7_FwwhqOG8ue5LIQ2mmd7AT8IvH7v1YkVBofp5CV334NEVTfhET--n221tt7YtVsY4zYvj7WknBr1fHOv3pdH3qo9EsZR4XWQRQcCnte4v3bYFRlkDUMuXkyDiNmmkpa5D7qEOrtds8Z_Yi3GaBkmj-LA6k_tVEA8V9SIt9NPGrna7Db0174bzgiqMRZ548H2Y4eaJXOSxWIy0dtj5BHIBjuEZcyYFUD6wXB12azfgfclZwk-ekVgMoq4_l8PpY_N1eCsBvHJ0i7H95qgwj8D2OPvpbbh3uZvgpMkEZ0Eie7kjf1bP8yOYG_NQPpF5-q0T8rGll7caS2W9OX6T2e5fX78k_1692B6X5OfZivlIXRxfLCLw_gKQ14Y538NMNSDIXpjBaN967P41fNdQo0XOlNK24ffe-_QlkNUxp2ylNLOukv1hz1UUWAsxSiulBZft8n_LPENXHGdwCY_fMTl39XIM-5ShZielXBpd1awefAUdIXvqSQDOz7mXviQ-6fAyWl_FoDDiYxskkOjk6TVf2J5aq-RvNWahCIDTCuoYYm9llu3vwf_xj6Sbd7MkZTH7fvgKwvJQmr_vnCBllxC2iGSs7Yzogb79Zr5XOTnuXg2KfWj1IEcy0Dmx24LryKlnkKmpWju6WKDf9CfoDPm9WbrmoedMpYcygYQgkRxnXzHPVHoMw8oDemMA75VBQfl88jgRduN8ET6Ckss1DUiW5UbdsA2pQMmwm1kPvDWA9ziKcgh_m9ZsaLCZPAP0dL8OmqSc4d8fA6YzNjdLXV7bVvlijjZgeoluYLeir6ayci3_aHEFfxokU=s0-d)
4- for a ideal LPF with f_0 = f_s/2, we have
5- Consequently the density of the quantization noise, which spreads over

is given by
where the quantization noise is a white process with the following RMS value:
[Candy'92] explains that due to sampling, the noise power folds into the frequency band
However, I have to find a self-convincing explanation.
Here is how I understand the impact of oversampling on quantization noise:
1- we should consider a LPF with the bandwidth of
2- the sampling/quantization system is as follows [x denotes the instantaneous quantization noise]:
x--|sampler|--|LPF: h[n]|--y
3- Auto-covariance of LPF output is given by:
4- for a ideal LPF with f_0 = f_s/2, we have
5- Consequently the density of the quantization noise, which spreads over
is given by
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