component selection
play

Component selection 1 (c) 2020 A.J.M. Montagne Component selection - PowerPoint PPT Presentation

Mar 17, 2024 •437 likes •1.27k views

Component selection 1 (c) 2020 A.J.M. Montagne Component selection + - + - + - 2 (c) 2020 A.J.M. Montagne Component selection - Noise: + - + - + - 3 (c) 2020 A.J.M. Montagne Component selection - Noise: + - + - + - 4

  1. Component selection 1 (c) 2020 A.J.M. Montagne

  2. Component selection + - + - + - 2 (c) 2020 A.J.M. Montagne

  3. Component selection - Noise: + - + - + - 3 (c) 2020 A.J.M. Montagne

  4. Component selection - Noise: + - + - + - 4 (c) 2020 A.J.M. Montagne

  5. Component selection - Noise: + - + - - Bandwidth: + - 5 (c) 2020 A.J.M. Montagne

  6. Component selection - Noise: + - + - - Bandwidth: - Accuracy: + - 6 (c) 2020 A.J.M. Montagne

  7. Component selection - Noise: + - + - - Bandwidth: - Accuracy: + - Drive capability: - 7 (c) 2020 A.J.M. Montagne

  8. Component selection - Noise: + - + - - Bandwidth: - Accuracy: + - Drive capability: - 8 (c) 2020 A.J.M. Montagne

  9. Component selection - Noise: + - + - - Bandwidth: - Accuracy: + - Drive capability: - 9 (c) 2020 A.J.M. Montagne

  10. Component selection - Noise: + - OPA211 + - - Bandwidth: 3.4nF - Accuracy: 600 + - Drive capability: - 10 (c) 2020 A.J.M. Montagne

  11. Component selection - Noise: + - OPA211 + - - Bandwidth: 3.4nF - Accuracy: 600 220 + - Drive capability: - 47uF 11 (c) 2020 A.J.M. Montagne

  12. Component selection - Noise: + - OPA211 + - - Bandwidth: 20k 3.4nF - Accuracy: 600 220 + - Drive capability: - 12 (c) 2020 A.J.M. Montagne

  13. Component selection - Noise: + - OPA211 + - - Bandwidth: 20k 3.4nF - Accuracy: 600 220 + - Drive capability: - 47uF 13 (c) 2020 A.J.M. Montagne

  14. Component selection - Noise: + - 20k OPA211 + - - Bandwidth: 20k 3.4nF - Accuracy: 600 220 + - Drive capability: - 47uF 14 (c) 2020 A.J.M. Montagne

  15. Component selection - Noise: + - 20k OPA211 + - - Bandwidth: 1uF 20k 3.4nF - Accuracy: 600 220 + - Drive capability: - 47uF 15 (c) 2020 A.J.M. Montagne

  16. Component selection - Noise: + - 1k 20k OPA211 + - - Bandwidth: 1uF 20k 3.4nF - Accuracy: 600 1k 220 + - Drive capability: - 47uF 16 (c) 2020 A.J.M. Montagne

  17. Component selection - Noise: + - 1k 20k OPA211 + - - Bandwidth: 1uF 20k 3.4nF - Accuracy: 600 1k 47uF 220 + - Drive capability: - 47uF 17 (c) 2020 A.J.M. Montagne

  18. Component selection - Noise: + - 1k 20k OPA211 + - - Bandwidth: 1uF 20k 3.4nF - Accuracy: 600 1k 47uF 220 + - Drive capability: - 47uF 18 (c) 2020 A.J.M. Montagne

  19. Modeling OpAmp 19 (c) 2020 A.J.M. Montagne

  20. Modeling OpAmp Small-signal dynamic behavior OPA211 20 (c) 2020 A.J.M. Montagne

  21. Modeling OpAmp Small-signal dynamic behavior OPA211 21 (c) 2020 A.J.M. Montagne

  22. Modeling OpAmp Small-signal dynamic behavior OPA211 8u 900n 1p + + 3.6k 60 0.7 20k 7.5p - - 1p A_0*(1+s/2/PI/40M)/(1+s/2/PI/120)/(1+2/2/PI/20M) 22 (c) 2020 A.J.M. Montagne

  23. Modeling OpAmp Small-signal dynamic behavior OPA211 8u 900n 1p + + 3.6k 60 0.7 20k 7.5p - - 1p A_0*(1+s/2/PI/40M)/(1+s/2/PI/120)/(1+2/2/PI/20M) .model OPA211_A0 OV + cd = 8p ; differential-mode input capacitance + gd = 50u ; differential-mode input conductance + cc = 2p ; common-mode input capacitance + av = {A_0*(1+s/2/PI/40M)/(1+s/2/PI/120)/(1+s/2/PI/20M)} ; voltage gain + zo = {3.6k/(1+s*3.6k*8u) + 0.7 + s*900n*60/(60+s*900n)} ; output impedance 23 (c) 2020 A.J.M. Montagne

  24. Modeling OpAmp SLiCAP noise and bias models 24 (c) 2020 A.J.M. Montagne

  25. Modeling OpAmp SLiCAP noise and bias models 25 (c) 2020 A.J.M. Montagne

  26. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias 26 (c) 2020 A.J.M. Montagne

  27. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage 27 (c) 2020 A.J.M. Montagne

  28. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current 28 (c) 2020 A.J.M. Montagne

  29. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current Mean value bias current 29 (c) 2020 A.J.M. Montagne

  30. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current Mean value bias current Standard deviation bias current 30 (c) 2020 A.J.M. Montagne

  31. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current Mean value bias current Standard deviation bias current SLiCAP O_noise nullor with equivalent input noise sources 31 (c) 2020 A.J.M. Montagne

  32. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current Mean value bias current Standard deviation bias current SLiCAP O_noise nullor with equivalent input noise sources Spectral density noise voltage 32 (c) 2020 A.J.M. Montagne

  33. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current Mean value bias current Standard deviation bias current SLiCAP O_noise nullor with equivalent input noise sources Spectral density noise voltage Spectral density noise current 33 (c) 2020 A.J.M. Montagne

  34. Modeling OpAmp SLiCAP noise and bias models SLiCAP O_dcvar nullor with o ff set and bias Standard deviation o ff set voltage Standard deviation o ff set current Mean value bias current Standard deviation bias current SLiCAP O_noise nullor with equivalent input noise sources Spectral density noise voltage Spectral density noise current 34 (c) 2020 A.J.M. Montagne

  35. SLiCAP noise veri fi cation 35 (c) 2020 A.J.M. Montagne

  36. SLiCAP noise veri fi cation 36 (c) 2020 A.J.M. Montagne

  37. SLiCAP noise veri fi cation Noise fi gure 2.4dB over 1.57x500kHz bandwidth. 37 (c) 2020 A.J.M. Montagne

  38. SLiCAP noise veri fi cation Noise fi gure 2.4dB over 1.57x500kHz bandwidth. 38 (c) 2020 A.J.M. Montagne

  39. SLiCAP biasing veri fi cation 39 (c) 2020 A.J.M. Montagne

  40. SLiCAP biasing veri fi cation 40 (c) 2020 A.J.M. Montagne

  41. SLiCAP biasing veri fi cation All component tolerances 1% (3-sigma) 41 (c) 2020 A.J.M. Montagne

  42. SLiCAP biasing veri fi cation All component tolerances 1% (3-sigma) Standard deviation of the output voltage: 10mV 42 (c) 2020 A.J.M. Montagne

  43. SLiCAP biasing veri fi cation All component tolerances 1% (3-sigma) Standard deviation of the output voltage: 10mV 43 (c) 2020 A.J.M. Montagne

  44. Frequency response 44 (c) 2020 A.J.M. Montagne

  45. Frequency response 8u 900n 1p 600 + + + 3.6k 60 0.7 20k 7.5p 3.4n - - - 1p A_0*(1+s/2/PI/40M) (1+s/2/PI/120)(1+2/2/PI/20M) 20k 220 45 (c) 2020 A.J.M. Montagne

  46. Frequency response LRC series resonance at 2.88MHz 8u 900n 1p 600 + + + 3.4n 3.6k 60 0.7 20k 7.5p - - - 1p A_0*(1+s/2/PI/40M) (1+s/2/PI/120)(1+2/2/PI/20M) 20k 220 46 (c) 2020 A.J.M. Montagne

  47. Frequency response LRC series resonance at 2.88MHz 8u 900n 1p 600 + + + 3.4n 3.6k 60 0.7 20k 7.5p - - - 1p A_0*(1+s/2/PI/40M) (1+s/2/PI/120)(1+2/2/PI/20M) 20k 220 47 (c) 2020 A.J.M. Montagne

  48. Frequency response #10 6 Root Locus 3 2 1 LOOPGAIN Poles Imag [Hz] LOOPGAIN Zeros SERVO Poles:A 0 = 1.0e+00 0 SERVO Poles:A 0 = 6.7e+05 SERVO Poles:A 0 = 1.0e+00 .. 6.7e+05 -1 -2 -3 -6 -5 -4 -3 -2 -1 0 Real [Hz] #10 6 48 (c) 2020 A.J.M. Montagne

  49. Frequency response #10 6 Root Locus 3 2 dominant pole 1 LOOPGAIN Poles Imag [Hz] LOOPGAIN Zeros SERVO Poles:A 0 = 1.0e+00 0 SERVO Poles:A 0 = 6.7e+05 SERVO Poles:A 0 = 1.0e+00 .. 6.7e+05 -1 -2 -3 -6 -5 -4 -3 -2 -1 0 Real [Hz] #10 6 49 (c) 2020 A.J.M. Montagne

  50. Frequency response #10 6 Root Locus 3 2 dominant pole 1 LOOPGAIN Poles Imag [Hz] LOOPGAIN Zeros SERVO Poles:A 0 = 1.0e+00 0 SERVO Poles:A 0 = 6.7e+05 SERVO Poles:A 0 = 1.0e+00 .. 6.7e+05 -1 -2 -3 -6 -5 -4 -3 -2 -1 0 Real [Hz] #10 6 non-dominant pole too close to ignore 50 (c) 2020 A.J.M. Montagne

  51. Frequency response #10 6 Root Locus 3 2 dominant pole 1 LOOPGAIN Poles Imag [Hz] LOOPGAIN Zeros SERVO Poles:A 0 = 1.0e+00 0 SERVO Poles:A 0 = 6.7e+05 SERVO Poles:A 0 = 1.0e+00 .. 6.7e+05 -1 -2 -3 -6 -5 -4 -3 -2 -1 0 Real [Hz] #10 6 non-dominant pole too close to ignore 51 (c) 2020 A.J.M. Montagne

  52. Frequency response Uncompensated ampli fi er 52 (c) 2020 A.J.M. Montagne

  53. Frequency response Magnitude plots 100 50 magnitude [dB] 0 -50 -100 -150 10 2 10 3 10 4 10 5 10 6 10 7 Phase plots 200 100 Uncompensated ampli fi er phase [deg] 0 -100 ASYMPTOTIC LOOPGAIN SERVO -200 DIRECT GAIN -300 10 2 10 3 10 4 10 5 10 6 10 7 frequency [Hz] 53 (c) 2020 A.J.M. Montagne

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

RESTORE Direct Component Project Selection Update Committee of the Whole 02/09/17 Overview

RESTORE Direct Component Project Selection Update Committee of the Whole 02/09/17 Overview

RESTORE Direct Component Project Selection Update Committee of the Whole 02/09/17 Overview August 11th COW- Commissioners decided to select two projects per commissioner for further assessment. October 13 th COW- Staff presented Risk

348 views • 31 slides
Principal Component Analysis (PCA) CE-717: Machine Learning Sharif University of Technology

Principal Component Analysis (PCA) CE-717: Machine Learning Sharif University of Technology

Principal Component Analysis (PCA) CE-717: Machine Learning Sharif University of Technology Spring 2016 Soleymani Dimensionality Reduction: Feature Selection vs. Feature Extraction Feature selection Select a subset of a given feature

5.23k views • 40 slides
Selection Rules: Selection Rules Each of the spectroscopies have associated selection

Selection Rules: Selection Rules Each of the spectroscopies have associated selection

Selection Rules: Selection Rules Each of the spectroscopies have associated selection rules. Selection rules originate from the quantum mechanical description of electromagnetic radiation interaction with matter. Use time-dependent

424 views • 13 slides
For use in AIM Awards centres Component Level: Level Three Component Guided Learning Hours: 28

For use in AIM Awards centres Component Level: Level Three Component Guided Learning Hours: 28

Qualification Component and Record of Learner Achievement Garden Design: Presentation Techniques L3(Y/504/1124) For use in AIM Awards centres Component Level: Level Three Component Guided Learning Hours: 28 Ofqual Component Reference No:

344 views • 6 slides
SELECTION Deterministic Stochastic Proportionate selection: Roulette Wheel Selection

SELECTION Deterministic Stochastic Proportionate selection: Roulette Wheel Selection

SELECTION Deterministic Stochastic Proportionate selection: Roulette Wheel Selection Rank based selection Tournament Selection COMPETITION (mu) denotes the size of the (parent) population (lambda) denotes the

390 views • 19 slides
For use in AIM Awards centres Component Level: Level Three Component Guided Learning Hours: 21

For use in AIM Awards centres Component Level: Level Three Component Guided Learning Hours: 21

Qualification Component and Record of Learner Achievement Instructing Violence Reduction Skills: Presentation Skills L3(T/505/0543) For use in AIM Awards centres Component Level: Level Three Component Guided Learning Hours: 21 Ofqual Component

346 views • 7 slides
ERP Selection KIRTANE & PANDIT Suhas Deshpande Why ERP Selection is important ?

ERP Selection KIRTANE & PANDIT Suhas Deshpande Why ERP Selection is important ?

ERP Selection What are the Key Elements ? Agenda Why ERP Selection is important ? Why ERP Selection is important ? Selection Criteria- General Selection Criteria- General Selection Criteria- General Selection Criteria-

319 views • 18 slides
CloudKeeper Modularity Architecture Select Component Details Component Diagram Interpreter API

CloudKeeper Modularity Architecture Select Component Details Component Diagram Interpreter API

CloudKeeper Modularity Architecture Select Component Details Component Diagram Interpreter API DSL interpret executable data workflow representation domain-specific language for structures, send atomic units (object model) and component

468 views • 44 slides
WIO IOSAP Project Budget Nairobi Convention WIO IOSAP Budget per Project Component COMPONENT

WIO IOSAP Project Budget Nairobi Convention WIO IOSAP Budget per Project Component COMPONENT

WIO IOSAP Project Budget Nairobi Convention WIO IOSAP Budget per Project Component COMPONENT BUDGET (USD) Component A- Critical habitats management 3,388,000 Component B - Improved water quality 2,310,000 Component C - Sustainable

456 views • 7 slides
Feature Selection: ROC and Subset Selection Theodoridis 5.5-5.7 Using ROC for Feature Selection

Feature Selection: ROC and Subset Selection Theodoridis 5.5-5.7 Using ROC for Feature Selection

Feature Selection: ROC and Subset Selection Theodoridis 5.5-5.7 Using ROC for Feature Selection Hypothesis Tests Examined (e.g. t-test): Useful for discarding features But does not tell us about overlap between classes for a feature! At Left

526 views • 21 slides
Functional components Notification component Application received Refuse ? Notification

Functional components Notification component Application received Refuse ? Notification

Functional components Notification component Application received Refuse ? Notification component Missing info component Refuse Info Available Pay component Issue ? Complete Assess Issue component Order Component Playbooks

401 views • 12 slides
Avoiding Component Failures Increasing field reliability Agenda A few words about Kluber

Avoiding Component Failures Increasing field reliability Agenda A few words about Kluber

Avoiding Component Failures Increasing field reliability Agenda A few words about Kluber Lubrication Lubrication Fundamentals Tribology Oils and Greases Lubricant Selection for Bearings and Gears Bearing Failure Modes and

832 views • 79 slides
Assembling Component Frameworks What is a Component Framework? Set of individual software

Assembling Component Frameworks What is a Component Framework? Set of individual software

Assembling Component Frameworks What is a Component Framework? Set of individual software modules that perform a specific task Can range from a few components to many Must be extensible Used for various purposes i.e. scientific

171 views • 14 slides
Rijndael Note on naming Vincent Rijmen Note on naming 1. Introduction After the selection of

Rijndael Note on naming Vincent Rijmen Note on naming 1. Introduction After the selection of

Joan Daemen Rijndael Note on naming Vincent Rijmen Note on naming 1. Introduction After the selection of Rijndael as the AES, it was decided to change the names of some of its component functions in order to improve the readability of the

762 views • 47 slides
Network Components Component Names Component Function Example Application, or app, user Uses

Network Components Component Names Component Function Example Application, or app, user Uses

Network Components Component Names Component Function Example Application, or app, user Uses the network Skype, iTunes, Amazon Host, or end-system, edge Supports apps Laptop, mobile, desktop device, node, source, sink Router, or switch,

1.25k views • 37 slides
Cross sectional Shape Selection Materials have properties Strength, stiffness, electrical

Cross sectional Shape Selection Materials have properties Strength, stiffness, electrical

Cross sectional Shape Selection Materials have properties Strength, stiffness, electrical conductivity, etc. A component or structure is a material made into a particular shape Different shapes are more or less efficient for

543 views • 27 slides
Dynamic Response of Structures With Frequency Dependent Damping Blanca Pascual & S Adhikari

Dynamic Response of Structures With Frequency Dependent Damping Blanca Pascual & S Adhikari

Dynamic Response of Structures With Frequency Dependent Damping Blanca Pascual & S Adhikari School of Engineering, Swansea University, Swansea, UK Email: S.Adhikari@swansea.ac.uk URL: http://engweb.swan.ac.uk/ adhikaris Schaumburg,

646 views • 37 slides
Application of Linear-Phase Digital Crossover Filters to Pair-Wise Symmetric y Multi-Way

Application of Linear-Phase Digital Crossover Filters to Pair-Wise Symmetric y Multi-Way

Presented at the 32 nd AES Conference, 2007 September 21-23, 2007, Hillerod, Denmark Application of Linear-Phase Digital Crossover Filters to Pair-Wise Symmetric y Multi-Way Loudspeakers Part 2: Control of Beamwidth and Polar Shape D. B.

319 views • 30 slides
Sound Field Synthesis for Jens Ahrens, Rudolf Rabenstein Audio Presentation and Sascha Spors Te

Sound Field Synthesis for Jens Ahrens, Rudolf Rabenstein Audio Presentation and Sascha Spors Te

Figure 1b: Photo of loudspeaker system used for research on sound field synthesis. Pictured is a 64-channel rectangular array at Signal Teory and Digital Signal Processing Group, University of Rostock Sound Field Synthesis for Jens Ahrens,

287 views • 11 slides
plenaries Speaker: Ms Audrey Zibelman CEO, Australian Energy Market Operator (AEMO) Abstract :

plenaries Speaker: Ms Audrey Zibelman CEO, Australian Energy Market Operator (AEMO) Abstract :

Presentation abstracts Keynote and Navigating the transition to the fourth revolution plenaries Speaker: Ms Audrey Zibelman CEO, Australian Energy Market Operator (AEMO) Abstract : Looking ahead, we as an industry are facing significant

469 views • 13 slides
Demonstration of 500 o C AC Amplifier Based on SiC MESFET and Ceramic Packaging Liang -Yu Chen

Demonstration of 500 o C AC Amplifier Based on SiC MESFET and Ceramic Packaging Liang -Yu Chen

Demonstration of 500 o C AC Amplifier Based on SiC MESFET and Ceramic Packaging Liang -Yu Chen David Spry Philip G. Neudeck OAI/NASA GRC OAI/NASA GRC NASA GRC 21000 Brookpark Rd., MS 77-1 21000 Brookpark Rd., MS 77-1 21000 Brookpark Rd., MS

667 views • 7 slides
FERC NOPR on Integrating Renewable Energy FERC NOPR on Integrating Renewable Energy Resources

FERC NOPR on Integrating Renewable Energy FERC NOPR on Integrating Renewable Energy Resources

FERC NOPR on Integrating Renewable Energy FERC NOPR on Integrating Renewable Energy Resources into the Transmission Grid Resources into the Transmission Grid Presented by: February 17, 2011 Floyd L. Norton, IV (202) 739-5620

640 views • 30 slides
Western Area Power Administration Upper Great Plains Region (Western-UGP) Transmission &

Western Area Power Administration Upper Great Plains Region (Western-UGP) Transmission &

Western Area Power Administration Upper Great Plains Region (Western-UGP) Transmission & Ancillary Services Formula Rates 2016 Update Customer Information Meeting October 29, 2015 9 a.m. MDT, Billings, MT and via Webex Introductions

1.13k views • 43 slides
energy October 2017 Drax Group Companies which change the way energy is generated, supplied and

energy October 2017 Drax Group Companies which change the way energy is generated, supplied and

Biomass as a large scale source of energy October 2017 Drax Group Companies which change the way energy is generated, supplied and used for a better future 2 Royal Society October 2017 Drax Power Station Once the largest carbon emitter in

480 views • 12 slides

More recommend