mixed-signal systems-on-chip: architectures and design toolsmixed-signal systems-on-chip:...

Mixed-Signal Systems-on-Chip: Architectures and Design Tools

Alex Doboli, PhDAssociate Professor

Department of Electrical and Computer EngineeringState University of New York, Stony Brook, NY 11794

Email: [email protected]

The “Wish List”

• A new development paradigm that:– Much more efficient design methodologies

– Higher Level Of Abstraction For Development

– Exploit design reusability & retargeting– Frees Designers From Low Level Implementation Details– Produces Interoperable And Portable Designs– Supports Reconfigurability– Provides Extensive, and Extensible, Third-Party Device

Libraries– Is Self-Documenting– Is scalable and extensible

Research, Entrepreneurial & Educational Challenges

• High-level AMS synthesis?

Stephan Ohr, “Synthesis proves to be Holy Grail for analog EDA”, EE Times, 06/08/1999

Research, Entrepreneurial & Educational Challenges

– AMS design is “art” and less “science” • Theory? Methodology? How to invent application-

specific topologies & circuits?

– Architectures?

– Few CAD tools (mostly simulators: SPICE etc.)• Transistor is not a switch! (modeling)• Design usually at a low level (transistors, layout)

– Who will develop the architectures & tools?

– Who designs AMS systems? EE/CE/CS/all?

Presentation outline

• PSoC: embedded mixed-signal architecture

• Related research at Stony Brook

• Systematic methodology for ∆Σ ADC design

• Automated circuit modeling

• Education & training

• Conclusions

Embedded Mixed-Signal Architectures

• Cypress’ programmable PSoC mixed-signal SoC

• Main features:– Hardware programmability

• Programmable analog blocks• Programmable digital blocks• Programmable interconnect • Programmable I/Os• Programmable clocks• Selectable power supply

– Integration as an SoC

PSoC Mixed-Signal Architecture

Why PSoC-like architectures ?

• PSOC supports/helps:

– Design reusability & retargeting (library)

– Predictable performance (library of models)– Frees designers from low level implementation

(pre-characterized cells)– Supports Reconfigurability– Supports extensive, and extensible, third-party

device libraries– Is scalable and extensible (programmable

transceivers, PLLs, software radio)


• Analog blocks are programmable– Programmable functionality (control registers)– Programmable inputs & outputs

• Analog blocks of two types– Continuous time blocks– Switched capacitor blocks (type C and type D)

• Connected to programmable I/O ports

• Programmable interconnect – Three kind of programmable interconnect

Programmable interconnect

Programmable CT blocks

Programmable SC blocks

Some CAD related Issues

• Higher Level Of Abstraction For Development– What behavioral descriptions are synthesizable? DAEs, TFs,

SFGs?

– How do you correctly mix together continuous time and discrete time descriptions?

– What standard specification notations? VHDL-AMS, Verilog-A, MATLAB/SIMULINK, UML?

• Frees Designers From Low Level Implementation Details– How do you synthesize a set of DAEs? (application-specific

topologies)

– Does the design work? (circuit modeling)

– CAD tools for transistor sizing and layout (Neoliniar CADENCE)

• Supports Reconfigurability– Reconfigurable AMS architectures, reconfigurable ADCs, filters

VLSI System Design Laboratory(http://www.ece.sunysb.edu/~vsdlab)

Lab director: Dr. Alex Doboli,Email: [email protected], Phone: 631-632-1611

Lab expertise: • Embedded mixed-signal systems • CAD for system&circuit optimization• Analog circuit modeling• IP core integration

Research funding (since 2001): • NSF Center for Design of Analog and Digital IC • AFRL • NSF ITR• DARPA, IBM, DAC, Cypress

Academic results:• 3 PhDs graduated • 3 MS graduated• 19 journal papers• 65 peer reviewed conference papers

IC Developments:• Rhapsody CAD tool (ADC design)• ISIS CAD tool (SoC integration)• SoC design in 0.18µm process

VLSI System Design Laboratory(http://www.ece.sunysb.edu/~vsdlab)

• G. Gielen, R. Rutenber, “Computer-Aided Design of Analog and Mixed-Signal Integrated Circuits”, Proceedings of IEEE, Vol. 88, No. 12, pp. 1825-1852, 2000:

“Recently, a first attempt was presented towards the full behavioral synthesis of analog systems from (annotated) VHDL-AMS behavioral descriptions”.

• A. Doboli, R. Vemuri, "Behavioral Modeling for High-Level Synthesis of Analog and Mixed-Signal Systems from VHDL-AMS", IEEE Transactions on CADICS, Vol. 22, No. 11, 2003, pp. 1504-1520. (among the most downloaded papers in 2005)

Rhapsody: Automated design of analog and mixed-signal systems

Topology generation andsystem architecture selection:

VHDL-AMS specifications:entity aaa is

…end entity;

Performance evaluation

Circuit and interconnect

models

Obtained performanceConstraint transformation,

floorplanning and global routing

Performance evaluation (simulation)

Rhapsody (snapshots)

Benefits

Quality:- Complements SD toolbox (finding NTF and STF), Cadence’s

NeoCircuit (transistor sizing) and NeoCell (layout generation) - Produces SD topologies customized to performance specific

requirements- less complex, better sensitivity to parameter variations, and

lower power consumption- Finds 6x-10x more constraint-satisfying solutions than

CircuitExplorer (Synopsis) and NeoCircuit (Cadence)Effort:

- Designs (including circuit sizing using NeoCircuit and layout using NeoCell) can be ready much faster

- Designers can focus on more challenging issues, like system topology, selecting circuits

- Can be used by less-experienced designers- Prototyping of new applications, e.g., reconfigurable SD ADC

Application-specific ∆Σ modulator topologies


• The topology is an SFG containing integrators and having all signal paths and coefficients of the signal paths defined

• A topology differs from another one in terms of the type of the integratorsignal paths definition numerical coefficients of the signal paths

• For topology synthesis, they are the control parameters and uniquely determine a topology

Traditional ∆Σ modulator topologies

3rd order Delta-Sigma modulator topologies (a) Chain of Integrators with Feedforward Summation (b) Chain of Integrators with Feedforward Summation and Local Feedback (c) Chain of Integrators with Distributed Feedback (d) Chain of Integrators with Distributed Feedback, Distributed

Feedforward and Local Feedback

Previous work: I

F. Medeiro, A. Verdu, A. Vazquez, “Top-down Design of High Performance Delta-Sigma Modulators”, Kluwer, 1999

• Designers have to select an incomplete topology based on experience

• Coefficient design is the only degree of freedom to be explored.

Set integrator types

Define signal paths

Explore coefficient values

An incomplete

topology is defined

Previous work: II

K. Francken, G. Gielen, “A High-level Simulation and Synthesis Environment for Delta-Sigma Modulators”, IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, Vol. 22, No. 8, 2003, pp. 1049-1061.

• Selected incomplete topologies (or complete topologies) are stored in a library

• Given design specifications, such as SNR and DR, the tool selects one with the smallest power consumption

Save and selectoptimal solution

Yes

No

Set integrator types

Define signal paths

Explore coefficient values

Meet Specification?

Previous work: III

O. Bajdechi, G. Gielen, J. Huijsing, “Systematic Design Exploration of Delta-Sigma ADCs”, IEEE Transactions on Circuits and Systems I, Vol. 51, No. 1, Jan 2004, pp. 86-95.

A filter level exploration of the design space defined by abstract topology parameters

implemented using theprevious design flow

Subject to sensitivityanalysis for yield

possible design solutions

Several best solutions interms of power consumption


• Y. Wei, H. Tang, A. Doboli, "DATE06: Systematic Methodology for Designing Reconfigurable Delta Sigma Modulator Topologies for Multimode Communication Systems", invited paper, IEEE Transactions on CADICS, accepted for publication.

• H. Tang, H. Zhang, A. Doboli, "Refinement based Synthesis of Continuous-Time Analog Filters Through Successive Domain Pruning, Plateau Search and Adaptive Sampling", IEEE Transactions on CAD of Integrated Circuits and Systems, Vol. 25, No. 8, pp. 1421-1440, August 2006.

• H. Tang, A. Doboli, "High-Level Synthesis of Delta-Sigma Modulators Optimized for Complexity, Sensitivity and Power Consumption", IEEE Transactions on CAD of Integrated Circuits and Systems, Vol. 25, No. 3, pp. 597-607, March 2006.

Proposed synthesis methodology

Define the solution space region with all the possible modulator topologies

Formulate topology synthesis as an MINLP (Mixed-Integer Nonlinear Programming) problem

Formulate the design requirements as symbolic cost functions and solve for the optimal solution

Advantages:• Global optimal solution is guaranteed• The methodology is scalable• The methodology could be fully automated

Generic topology for 3rd order modulator

adder the to from tscoefficien dfeedforwar andfeedback :adder theinput to from tscoefficien dfeedforwar :

adder theoutput to from tscoefficienfeedback :quantizer theinput to :

integrator theofoutput the:

thjji

thi

thi

thi

iYtibia

YiY


Chain of Integrators with Feedforward Summation


Chain of Integrators with Distributed Feedback


Chain of Integrators with Feedforward Summation and Local Feedback


Chain of Integrators with Distributed Feedback, Distributed Feedforward and

Local Feedback

Generic topology

(1) 1,...,1 ,0 ,01,...,1 ,,...,1 , if ,01,...,1 ,,...,1 , if ,0

+=≥≥+==<≤+==≥≥

NibaNiNjijtNiNjijt

ii

ji

ji

)()()()()()()()1(

)]()()()()([)(

)1()]()()()()([

)(

)1()]()()()()([

)(

33422411444

333223113333

332222112222

331221111111

zEzYtzYtzYtzVazubzVz

zYtzYtzYtzVazubzY

zzYtzYtzYtzVazub

zY

zzYtzYtzYtzVazub

zY

+×−×−×−×−×=−

×−×−×−×−×=

−×−×−×−×−×

=

−×−×−×−×−×

=

Symbolic TF for generic topology

• Generalize the symbolic expressions for Delta-Sigma modulator of any order

• For example, the numerator and denominator of of Delta-Sigma modulator of order are:

• Solve the above equation using MATHEMATICA toobtain and

dNTFnNTF

N

)()(zEzVNTF =

)()(zUzVSTF =

N

∑∑ ∑

∑∑

≠ ≠

−−−

=

−−

=

+

−+

+−−=

N

i

N

i

KNN

iiiiiiiiiiiii

KKKN

K

N

iii

KN

N

K

KN

Kn

ztttC

tCCNTF

K

KKKK

1 2

21212211)......)1(

...()1(

),...,,(),...,,(),...,,(

1

11

0

1

KNN

i

N

i

N

iiiiiiiiiiiii

KKKN

K

N

iii

KN

KN

KN

N

j

N

j

N

jjjjjjjjjjjjj

N

ii

KKKN

N

i

N

j

N

jjNjjNji

KN

N

iNii

KN

K

N

i

N

i

N

iiiiiiiiiiiii

KKKN

KN

iii

KN

N

K

KN

K

d

ztttC

tCCatttaC

ttaCtaC

tttCtCC

NTF

K

KKKK

K

KKKK

K

KKKK

−

≠ ≠

−−

=

−−

++

≠ ≠=

−−

= ≠++

−−

=+

−−

+

≠ ≠

−−

=

−−

=

+

∑∑ ∑

∑∑∑ ∑∑

∑ ∑∑∑

∑∑ ∑∑∑

−+

+−−+

+++−+

+−++−−

=

))......)1(

...()1()......

...()1(

...)......)1(...(()1(

1 2

21212211

1 2

21212211

1 2

2221

1 2

21212211

),...,,(),...,,(),...,,(

1

11

11),...,,(),...,,(),...,,(

1

1),1(),1(

22

11,

11

1

),...,,(),...,,(),...,,(1

11

0

1

Symbolic NTF and STF

• Complexity of growth of terms is roughly 4×N !

• Generally, modulator order is kept below 6~8, the symbolic expressions scale reasonably well

• MINLP is able to handle large number of constraints of different complexity

• Symbolic expressions for other combination of integrator types can be readily obtained via variable substitution

MINLP problem formulation

• By equating the symbolic TF to the desired TF, 3×(N+1) equations are obtained with (N+1)×(N+2) unknowns

• Properties for the symbolic TF:All terms are nonlinear expressions of the defined coefficient variables No quadratic terms

• This mild nonlinear formulation is suitable to be solved by NLP

• To select the signal paths, binary variables are defined, so MINLP

MINLP formulation

};1,0{ ),1( : ;0),( :

;0)( : ),( minimize

∈≤

=

ii

ii

i

ii

wxsatisfyxtosubjectwxxhtosubject

xgtosubjectwxxf

Linear constraints h are added considering the complexity of nonlinear product term Instead, we formulate h as:

small very is whereotherwise, 0 and if 1 εε =≥= iii wxxwxii wxx ×

Topology exploration flow

Initialize modulator order

Initialize combination

Initialize OSR

Initialize MAG

Generate NTF

Generate and solveMINLP program

Meet SNR, DR specification

Save solution

Go to next combination

Optimal solution

No

Yes

Yes

SNR, DR specification

Increase order

More combinations

Increase MAG

Increase OSR

YesYes YesYesNo No NoOSR in

rangeOrder

in rangeMAG in range

Minimum sensitivity test

Minimum power test

Minimum path test

Topology refinement

• Scaling => the voltage swings at the output of each integrator are within a certain range

• Nonideal blocks in Simulink

Experiment: 3rd order modulator

• 3rd order Delta-Sigma modulator• DR>70db•

• The topology is able to achieve DR=72db and SNR=67db

5.1 ,32 , ,5.1 ,3 max

==

===

MAGOSRVUPN ref

)6629.0529.1)(6685.0()1994.1)(1(

2

2

1 +−−+−−

=zzzzzzNTF

Optimal topology

A. Minimum signal path (topology not unique)

Optimal topology

B. Minimum sensitivity

Sensitivity cost function values are 1.723 and 2.250 respectively, with all (good case)L. Huelsman, “Active and Passive Analog Filter Design”, McGraw Hill, 1993

0.1 , ≤j

i

j

i

qx

Px SS

Topology from Toolbox

0.1, 3

34

3

3≥q

tqa SS

Sensitivity cost function values is 4.454, with some terms larger than 1.0, e.g.

R. Schreier, “The Delta-Sigma Toolbox 6.0”, www.mathworks.com/matlabcentral/fileexchange, Nov 2003.

Triple-mode continuous-time ∆Σ modulator

270kHz14.5/87EDGE190kHz15/90GSM615kHz13/80CDMA2000

1.92MHz11.5/70UMTSBandwidthDR (bits/dB)Mode

Design Specifications

484028, 32564484049664483--128962

GSM-EDGECDMA2000UMTSOSR

OrderDesign parameters

for possible candidates

[1] R. Veldhoven, “A Triple-Mode Continuous-Time ∆Σ Modulator With Switched-Capacitor Feedback DAC for a GSM/EDGE/CDMA2000/UMTS Receiver”, JSSC, Dec 2003.

Reconfigurable ∆Σ modulator topologies (1)

ΣΣ

0.5917−m10.5316−m20.5878−m3

1.3013−m21.3009−m1

1.3944−m3

ΣV

Σ ΣΣ

0.0785−m1

0.0442−m20.0360−m30.0013−m3

0.0024−m2

0.0069−m1

0.3420−m1

0.3605−m20.4008−m3

U

− 0.1970−m1

0.1667−m3 0.1670−m2

0.7720−m3

0.1552−m2

0.1324−m3 0.1364−m1

0.6244−m1 0.6585−m2

0.7987−m1

0.8988−m2 0.8762−m3

− −0.6274−m1

0.5261−m3 0.5133−m2

0.4815−m3

0.5159−m1, m2

0.4815−m3 m2 0.5159−m1,

0.1533−m3 0.1467−m2

0.1200−m1

Topology opt1

Generated topologies

Σ ΣΣ

U

−Σ Σ

0.0206−m3

0.0342−m1

0.0324−m2

ΣV

0.0349−m1

1.3000−m31.2080−m2

0.4545−m10.5556−m20.5163−m3

0.4545−m1

m2,m3

0.2222−m1

0.1829−m3 0.1769−m2

1.3000−m2,m3

0.4500−m1

0.4000−m1

1.4765−m10.5556−m20.5163−m3

1.3408−m10.1545−m1

0.8655−m2 0.8000−m3

0.4789−m2 0.4771−m3

−

0.5706−m1 0.5143−m2 0.6009−m3

0.0941−m2 0.0836−m3

0.2647−m1

0.0108−m1

−

Topology opt2

Design complexity

ηd : estimated design effort

ηp : estimated power consumption reduction

Np : # of signal pathsNp,r : # of non-reconfigurable cellsNc,r : # of reconfigurable cells

SNR degradation due to circuit noise

10−3

10−2

−150

−100

−50

0

Frequency (f/fs)

Spe

ctru

m (

dB)

Output spectrum comparison

opt2, noise = −60dB[1], noise = −60dBopt2, noise = −70dB[1], noise = −70dBopt2, ideal[1], ideal

−40 −35 −30 −25 −20 −15 −10 −5 0−10

0

10

20

30

40

50

60

70

80

90

Input amplitude [dB]S

NR

[dB

]

SNR comparison for different noise level

opt2, ideal[1], idealopt2, noise = −60dB[1], noise = −60dBopt2, noise = −50dB[1], noise = −50dB

Improvement as compared to the state-of-art design:3dB for the case of -60dB noise level5dB for the case of -50dB noise level

Experiments

Experiments

• Compare the triple-mode modulator with three single-mode modulators obtained with ∆Σ toolbox

– Design effort can be less than 1/3

– Complexity can be as less as 40%

– Power saving can be as large as 24.2%

– More robust to circuit nonidealities

Experiments using PSoC

• Implementation of a reconfigurable Delta-Sigma modulator using PSoC mixed-signal SoC

Experiments using PSoC

Education & Training

• Hugo De Man, “System-on-Chip Design: Impact on Education and Research”, IEEE Design & Test of Computers, July-Sept. 1999.

• System architects/designers:– Cross-disciplinary background: EE/CE/CS

– EE: signals, conversion techniques, impact of circuit nonidealities, and know how to tackle these

– CE: how HW & SW work together, abstraction levels, successive refinement, trade-off analysis

– CS: high-level description languages for systems, system and circuit modeling techniques, formal verification

Education & Training

Curricula, textbooks, projects, lab material, exercises?

A. Doboli and E. Currie“Embedded Mixed-Signal Systems: A Designer’s

Perspective”, due to be published in 2007.

Conclusions

• PSoC: embedded mixed-signal architecture

• Related research at Stony Brook

• Systematic methodology for ∆Σ ADC design

• Automated circuit modeling

• Education & training

Automated Macromodeling

• Produced macromodels:– Structural– No feedback dependencies (decoupled)– Symbolically characterized nonlinear current

sources– Extensible, accuracy is controllable– Insight into circuit– Reusable


f(vin)vin vout

Black-box macromodel

Vin1

v3v2

Vin2v1

VbnM5

M3 M4

M2M1

Circuit netlist Structural nonlinear macromodel

id3nkΣ

kid1nkΣ

kiCdb3nkΣ

kiCdb1nkΣ

k

id4nkΣ

kid2nkΣ

kiCdb4nkΣ

kiCdb2nkΣ

k

id3nkΣ

kid4nkΣ

kiCsb3nkΣ

kiCsb4nkΣ

kid1nkΣ

kiCdb5nkΣ

k

(sCgd4−gmg4)vin2 v1

gms4 C3(sCgd2−gmg2)v2

R3

(sCgd3−gmg3)vin1 v1

gms3 R2C2

V2

V3

C1 R1

V1

(sCgs3+gm3)vin1

(sCgs4+gm4)vin2

gmd3v2,eq

gmd4v3,eq

sCgd2v3,eq

(R2,C2)


v3

sCgd2v3

d3nk

iΣk

d4nk

iΣk

d5nk

iΣk

d3nk

iΣk

d1nk

iΣk

d4nk

iΣk

d2nk

iΣk

v3

(sCgd3−gmg3)vin1

gms3v1

C2 R2

v2

(sCgd4−gmg4)vin2

gms4v1

(sCgd2−gmg2)v2

C3 R3

v1

C1 R1

Σk

db5nkCCΣ

ksb4nkCΣ

ksb3nk

CΣk

db3nk CΣ

kdb1nk

CΣk

db2nkCΣ

kdb4nk

(sCgs3+gmg3)vin1

(sCgs4+gmg4)vin2

gmd3v2

gmd4

Vin1

v3v2

Vin2v1

VbnM5

M3 M4

M2M1

Circuit netlist


103

104

105

106

107

108

−100

−90

−80

−70

−60

−50

−40

Frequency [Hz]

HD

[dB

]

Comparison of HD2, HD3

HD3, SPICEHD3, modelHD2, SPICEHD2, model

Comparison of HD2, HD3


vout

Vin2

v3

RL CL

CfRfv2

Vin1

v4

v1

M1 M2

M6

M8

M9M7M5

M3 M4

Vbn


vout

d3nk

iΣk

d4nk

iΣk

d5nk

iΣk

CΣk

sb3nk CΣ

ksb4nk CΣ

kdb5nk

CΣk

db3nk CΣ

ksb2nkCΣ

ksb1nk

d3nk

iΣk

d1nk

iΣk

v3

d2nk

iΣk

d4nk

iΣk

CΣk

db2nk CΣ

kdb4nk

d8nk

iΣk

CΣk

sb8nk CΣ

kdb9nk

d9nk

iΣk

d6nk

iΣk

d7nk

iΣk

CΣk

db6nk CΣ

kdb7nk

gms3v1 (sCgd2)v3eq C2 R2(sCgd3−gmg3)vin1

vin1 Vin2 C1 R1(sCgs3+gmg3) (sCgs4+gmg4) gmd3 gmd4

v2eq v3eq

v1

v2

(sCgd2−gmg2)v2

(sCgd4−gmg4)vin2 gms4v1

(s(Cf+Cgd8)(1/Rf))v4eq C3 R3

(sCgs8+gmg8)v4 C5 R5

+1/Rf−gmg6)v3(sCgs8)v5eq

(s(Cgs6+Cf)

v4

C4 R4


103

104

105

106

107

108

−300

−250

−200

−150

−100

−50

0HD2 contribution

Frequency [Hz]

HD

2 [d

B]

M8M5M6M3,4M1,2HD2, modelHD2, SPICE


vout

Vin2

v3

RL CL

CfRfv2

Vin1

v4

v1

M1 M2

M6

M8

M9M7M5

M3 M4

Vbn


103

104

105

106

107

108

−110

−100

−90

−80

−70

−60

−50

−40

−30

−20

Frequency [Hz]

HD

[dB

]HD3 comparison for two−stage OpAmp

SPICEmodel


vo1

CΣk

db3nk

d7nk

iΣk

d3nk

iΣk

CΣk

db7nk

CΣk

db9nk

d11nk

iΣk

d9nk

iΣk

CΣk

db11nk

d1nk

iΣk

d3nk

iΣk

d5nk

iΣk

CΣk

sb1nkCΣ

ksb3nk CΣ

kdb5nk

CΣk

db1nk

d13nk

iΣk

d1nk

iΣk

CΣk

db13nk

v1

gms3

v1,eq(sCgd3−gmg3)

vin1

sCgd9v3,eq R5 C5

sCgd1vo1,eq

(sCgd9−gmg9)

v5

sCgs1v1,eq C3R3

(sCgs3+gmg3)vin1

(sCgs1+gm1)v3

gmd3v5 R1

gmd1vo1,eq C1

v3

(sCgd1−gmg1) gms1v1 Ro1 Co1

v5

v3

M13

R

Vo1 Vo2

ClCl

v1 v2

v4v3

v6

Vin1 Vin2

M1 M2

M14

M3

M9

M11 M6 M12

M4

M8

M10

M7

v5

Vbp Vcmfb Vbp

VbnM5


103

104

105

106

107

108

−90

−80

−70

−60

−50

−40

−30

−20

−10

0

Frequency [Hz]

HD

[dB

]HD3 comparison for OTA

HD3, SPICEHD3, model


1 2 3 4 5 6 7 80

5

10

15

20

25

30

35

40

45

50

Number of iteration

Err

or [%

]complexity vs. accuracy

single−stage, up to 100MHzsingle−stage, up to 10MHztwo−stage, up to 100MHztwo−stage, up to 10MHz

mixed-signal systems-on-chip: architectures and design toolsmixed-signal systems-on-chip:...

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