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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, VOL. 20, NO. 10, OCTOBER 2012 1835

A New Self-Healing Methodology for RF AmplifierCircuits Based on Oscillation Principles

Abhilash Goyal, Student Member, IEEE, Madhavan Swaminathan, Fellow, IEEE, Abhijit Chatterjee, Fellow, IEEE,Duane C. Howard, Student Member, IEEE, and John D. Cressler, Fellow, IEEE

AbstractThis paper proposes a new self-healing methodologyfor embedded RF amplifiers in RF sub-systems. The proposedmethodology is based on oscillation principles in which thedevice-under-test (DUT) generates its test signature with thehelp of additional circuitry. In the proposed methodology, the

self-generated test signature from the RF amplifier is analyzedby using on-chip resources for testing and controlling its calibra-tion knobs to compensate for multi-parameter variations in themanufacturing process. Thus, the proposed methodology enablesself-test and self-calibration/correction of RF amplifiers without

the need for an external test stimulus, enabling true self-healingRF designs. The proposed methodology is demonstrated through

simulations as well as measurements performed on an RF LNA,which were designed in a commercially-available SiGe BiCMOSprocess technology.

Index TermsAgilents Advance Design System (ADS), Ca-dence, low noise amplifier (LNA), MATLAB, oscillation-based test,oscillations, self-calibration, self-healing, self-testing, yield.

I. INTRODUCTION

D URING the past decade integrated circuit (IC) technologyhas progressed in accordance with Moores law. Theseadvances in IC technology have enabled the integration of RFfront-ends with multiple analog and digital blocks on a single

chip. For high speed operation, CMOS technology has evolved

from 250-nm device feature size to 32 nm and now utilizes

complicated strain engineering approaches. In addition, silicon

BJT technology has evolved with the addition of silicon-germa-

nium (SiGe) heterojunction bipolar transistors (HBTs). How-

ever, there are discouraging predictions regarding the effects of

process variations on the yield in deep-submicrometer ICs pro-

cesses [1][4]. RF circuits, in particular, are increasingly prone

to process variations, suffering from significant loss of para-

metric yield, as shown in Fig. 1. As a result of the increasing

concerns about yield, there is an increasing demand for self-

testing of RF circuits in order to minimize testing and produc-tion costs of RF circuits/systems because the test-setup cost is

quite high and make up approximately 30% of the total manu-

facturing costs [4], [5].

Manuscript received October 28, 2010; revised March 23, 2011; acceptedJune 14, 2011. Date of publication December 13, 2011; date of current versionJuly 19, 2012.

The authors are with the School of Electrical and Computer Engi-neering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail:[email protected]; [email protected];[email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVLSI.2011.2163953

Fig. 1. Low yield because of widening gap in the performance [1].

Fig. 2. Prior self-calibration/healing methods for RF amplifiers [6][8].

Previously, a general idea for on-chip calibration/correctionof mixed-signal circuits was proposed in [5], and healing

methods for improving the manufacturing yield of RF sub-sys-

tems, namely embedded low noise amplifiers (LNAs), have

been proposed in [6][8]. In these healing methods, an input

stimulus is applied to calibrate the embedded LNA and the

output response is captured using embedded sensors. With the

help of on-chip circuit resources, the tuning knobs of the

LNA are adjusted to compensate for performance loss due

to the effects of process variation. Although the methods of

[6][8] help in improving the yield of the embedded LNA,

all of these methods assume the availability of an RF-signal

source (see Fig. 2). In addition, loop-back of the transmitter

output to the receiver input is necessary for testing the receiver,

making the testing process cumbersome. Furthermore, the RF

stimulus source (the RF transmitter) needs to be tested first,

before testing the embedded LNA.

In the present paper, a new self-healing methodology is pro-

posed for RF amplifiers to overcome the need for an external RF

test stimulus. In this paper, the device undertest (DUT)is a LNA

as shown in Fig. 3. The proposed technique does not require the

use of an external test stimulus for performing self-healing be-

cause the stimulus is generated by the RF amplifier itself with

the help of additional circuitry and by using oscillation princi-

ples. This stimulus is used to assess the impact of process vari-

ations on the performance of the amplifier. Subsequently, com-

1063-8210/$26.00 2011 IEEE

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1836 IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, VOL. 20, NO. 10, OCTOBER 2012

Fig. 3. Proposed self-healing architecture in this paper.

pensation for loss of performance due to process variations is

performed by adjusting calibration/tuning knobs of the RF am-

plifier (DUT). This tuning knob manipulation is driven by anal-

ysis of the self-generated output (referred to as the DUT signa-

ture) from the amplifier, as shown in Fig. 3. The output signa-

ture of the DUT is analyzed with available on-chip resources

such as the mixer, ADC, PMU, and DSP. Thus, the proposed

methodology enables self-test as well as self-calibration/correc-

tion of the embedded LNA, leading to truly self-healingRF de-

signs. In the past, oscillation-based testing has been proposedfor analog/mixed-signal/RF circuits in [9][13]. The proposed

methodology in the present paper is also based on oscillation

principles, but for the first time, a self-healing methodology for

yield improvement of active RF circuits such as an embedded

RF LNAs has been proposed. Also, to achieve the yield im-

provement, the concept of performance curves is introduced.

In the following sections, the proposed self-healing method-

ology is explained, and the self-healing LNA architecture and

generation of control signals for the calibration/tuning knobs

are described. Simulation and measurement results are then pre-

sented, and finally a chip prototype is demonstrated.

II. PROPOSED SELF-HEALING METHODOLOGY

A. Self-Healing Flow

Fig. 4 shows the overall self-healing procedure, which in-

cludes both self-testing and self-calibration/correction. After

fabrication, the LNA is tested to identify those LNAs with

catastrophic vs. parametric defects. The proposed self-calibra-

tion/correction is exercised only if the LNA is determined to be

free of catastrophic faults.

B. Self-Healing Architecture

Consider the proposed self-healing architecture of the em-

bedded LNA, as shown in Fig. 5 [14]. In the self-healing mode,

an external feedback is enabled across the LNA such that this

feedback causes the LNA to oscillate and produce a sinusoidal

signal for testing and calibration purposes. The feedback net-

work is a simple phase shifter and can be implemented on-chip

or off-chip on a circuit board. The phase-shifter is connected

to the input and output ports of the LNA by RF switches to

complete the feedback loop. The sinusoidal signature from the

LNA output is down-converted and the FFT of the down-con-

verted signal is computed by the base-band DSP to determine

the oscillation frequency of the LNA with the enabled feedback.

It can be seen from Fig. 5 that all of the above can actually

Fig. 4. Self-healing flow.

Fig. 5. Proposed self-healing LNA architecture.

be accomplished without the use of an external RF test stim-ulus. Self-healing of the LNA in the presence of process varia-

tions is achieved by adjusting the LNAs tuning knobs using

on-chip power management unit (PMU), the on-chip RF mixer,

ADC, low-pass filter, image rejectfilter, and DSP, resources that

are necessary for performing normal wireless communications

functions.

In the proposed architecture, a phase shifter is designed in the

following manner.

1) With the inclusion of the phase shifter in the external

feedback, the LNA oscillates near to the desired oper-

ating or testing frequency.

2) The oscillation frequency of the LNA with the includedexternal feedback is sensitive to process variations and

differs significantly for the golden LNA and LNAs with

parametric failures.

3) The phase shifter design is robust to manufacturing

process variations and may be located on the circuit

outside the chip. In the present paper, we have demon-

strated the self-healing methodology assuming an

on-chip phase-shifter and it is subjected to the same

process variation as of the RF amplifier.

4) The change in the oscillation frequency due to para-

metric defects in the LNA is measurable by the receiver

chain and must lie within the bandwidth of the receiver

chain and be detectable using the on-chip ADCs sam-

pling speed and resolution).

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GOYAL et al.: NEW SELF-HEALING METHODOLOGY FOR RF AMPLIFIER CIRCUITS 1837

In our experiments, estimation of the maximum and

minimum change in the oscillation frequency due to

parametric defects around the desired testing frequency

is performed using Monte Carlo simulations. In practice, os-

cillation frequency measurements made on LNA samples se-

lected from different production lots and across a large number

of wafers should be used to determine the values of and

.

C. Classification of Catastrophic and Parametric Failures

In the proposed healing methodology, the detection of failures

is performed using predictive oscillation-based testing.

In an RF design, the RF performance/specifications are de-

pendent on the functional behavior of active and passive com-

ponents (inductors and capacitors) at the desired operating fre-

quency. In addition, RF specifications are interdependent since

a change in one specification causes a change in the others [15].

Let us assume that the nonlinearity of the amplifier is up to third

order, as given by (1). The P1 dB of the amplifier can be ap-proximated by (2). It can be inferred from these equations that

the P1 dB is dependent on the gain of the LNA, which

shows interdependency of different specifications. In addition,

at the circuit level, components that contribute to one specifi-

cation also contribute to the other specifications to some extent.

This increases the interdependency of RF specifications on each

other

(1)

(2)

where, in (1) and (2), is the gain of the amplifier; and

are nonlinearity coefficients

In active circuit design, some of the ac characteristics such

as the transistors transconductance is dependent on DC

biasing. As shown in (3) and (4), respectively, the of the

HBT and CMOS depends on the bias current tofirst order. Thus,

the gain of the R Fa mplifier is dependent on the bias current

as well, which shows interdependency of RF specifications on

biasing

(3)

(4)

In (3) and (4), is the beta or current gain of the HBT,

is the base current of the HBT, , is the width

of the MOS transistor, is length of the MOS transistor, is

the mobility of the transistor, is the gate capacitance of the

transistor, is the drain current of a MOS transistor and is

the collector current of the HBT.

Because of process variations, assume that changes to

, changes to , to ), to .

Hence, the RF amplifiers gain and P1 dB become a function

of process variations such as gain , as shown

in Fig. 6. For oscillations to initiate, the closed-loop gain of the

Fig. 6. Performance prediction using nonlinear mapping.

system has to be greater than unity and satisfy the Barkhausen

criterion. As the oscillations build up, the nonlinearity of the

system causes these oscillations to become stable. Thus, in our

methodology, the oscillation frequency also shifts because of

the change in the performance of the RF amplifier.

Since this shift in oscillation frequency is preserved during

RF down-conversion, there exists a finite possibility to derive

nonlinear analytical expressions which compute the relationshipamong the amplifier specifications, the oscillation frequency

and the bias current of the amplifier. However, the derivation

of such analytical expressions is difficult, and if the amplifier

has higher than third-order nonlinearity, then derivation of an-

alytical equations can become intractable. However, develop-

ment of a nonlinear model to predict specifications of RF ampli-

fiers can be accomplished using successive-learning processes

such as multivariate adaptive regression splines (MARSs) [16]

and artificial neural networks (ANNs) [17]. This concept of per-

formance prediction has been previously applied for mixed-

signal/RF testing based on alternate testing and predictive os-

cillation testing in [11][13], [18][20]. In addition, in [12] and[20] it is shown that accuracy of prediction depends on the

choice of the successive-learning processes and number of test

signature variables such as number of sinusoidal input tones, os-

cillation frequency and dc current.

In our approach, the phase shifter is designed in such a way

that catastrophic defects in the LNA cause no oscillations to

occur, while parametric defects lead to a change in the oscil-

lation frequency of the LNA with feedback around the response

of the golden LNA. The information about the DC power con-

sumed by the RF amplifier is assumed to be available from the

on-chip power-management unit (PMU). The specifications of

the embedded LNA are predicted from the nonlinear predictionmodel that maps its performance specifications to the observed

change in the oscillation frequency and DC current supplied by

the PMU to the LNA. For demonstration purposes, the non-

linear prediction model that predicts the specifications of the

embedded LNA is developed using successive-learning process

based on MARS in this paper. The MARS algorithm uses an ini-

tial recursive partitioning during training to gradually add basis

functions using forward stepwise placement; a backward proce-

dure is then applied and the basis functions associated with the

smallest increase in the least squares fit are removed. Further

details regarding MARS can be found in [16].

The development of a nonlinear prediction model using

MARS is a one time effort which requires a training set of

RF amplifiers with statistically likely parametric failures. In

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our experiments, the training set used to estimate the max-

imum and minimum change in the oscillation

frequency due to parametric defects around the desired testing

frequency is also used for the development of the pre-

diction models. As discussed earlier, RF amplifiers for which

oscillations do not occur are considered to have catastrophic

failures, while amplifier samples which result in oscillations are

assumed to have parametric failures. Self-correction/calibra-

tion of the amplifier (with parametric failures) is performed by

adjusting the tuning knobs designed in the amplifier according

to the procedure described below.

D. Self-Calibration Procedure and Tuning Knob Selection

Self-healing RF circuits require design with calibra-

tion/tuning knobs through which their performance can be

controlled post-manufacture by adjusting the tuning knob

values. Calibration/tuning knob control algorithms must also

be devised that allow the performance specifications of RF

circuits to be compensated to the maximum extent possibleusing such post-manufacture tuning knob control. In the best

case scenario, the design of tuning knobs that can control the

specifications of a DUT independent of other specifications is

desired. This allows all specifications to be controlled indepen-

dently post-manufacture, allowing maximum yield recovery.

However, in analog/RF circuits, different performance metrics

of the circuits are interdependent, thereby making it nearly

impossible to find tuning knobs which satisfy the above prop-

erty. For particular applications, certain specifications of the

RF circuits in question are important, while others are not.

For example, a gain specification of the LNA can be stringent

in certain applications, while other specifications (such as P1dB and noise figure) have sufficient performance margins. In

this case, a tuning knob can be introduced to vary the gain

of the LNA with less concern for P1 dB and noise figure.

For multi-specification designs, multiple tuning knobs may be

necessary and optimal tuning solutions should be found to meet

the required yield criteria.

The calibration/correction procedure can be one-time or iter-

ative. With the iterative procedure, the calibration/tuning knobs

are adjusted and measurements are made during each iteration

until the requiredperformance of the DUT is achieved. This pro-

cedure gives high yield recovery but is time consuming. In the

one-time calibration procedure, after analyzing the response ofthe DUT, the tuning knobs are programmed only once.

In the present paper, we have proposed a new one-time cal-

ibration procedure which works as follows: After the specifi-

cations of the DUT are predicted using the nonlinear predic-

tion model, the DUT calibration/tuning knobs are adjusted using

the performance curves of the DUT and (5). The performance

curves of the DUT reveal the changes in the specifications of the

DUT as functions of the DUT tuning knob values (one function

for each DUT specification) and are obtained by changing the

tuning knob values of the golden DUT and observing how its

performance specifications are affected. To estimate the change

in each specification of the DUT, the weighted sum of the ef-

fect of each DUT tuning knob is computed according to (5).

Equation (5) is a first-order linear approximation of the effect of

Fig. 7. Performance curves of LNA for different calibration knobs.

multiple tuning knob perturbations on each specification of the

DUT. For multi-specification optimization, a set of such linear

simultaneous equation can be solved

(5)

where is weight associated with the change in the tuningknob and is required compensation in the specifi-

cation . This is uniformly calculated for all DUTs as the

difference between the desired value of the specification and

the predicted value of the specification from the nonlinear pre-

diction model.

For efficient functioning of this calibration procedure, the

tuning knobs can be chosen such that each DUT specification

is largely dependent on only one knob. In that case, (5) reduces

to . Furthermore, the tuning knobs should be

chosen so that thespecifications of the DUT vary almost linearly

with the tuning knob values (even in the presence of nonlineari-

ties in the DUT, the linear assumption gives yield improvement

as shown in the following sections).To illustrate, let us consider embedded RF amplifier with two

knobs, Knob1 and Knob2. In this illustration, Knob1 is designed

to tune gain of the LNA and it tunes a capacitance in the am-

plifier. The Knob2 tunes power supply of the amplifier and is

designed to tune some other specification of the LNA (such

as P1 dB). Although Knob1 is to control gain, to account for

knob-to-knob interaction the calibration in the gain can

be performed versus (6). The change in the gain of the amplifier

because of Knob1 and Knob 2 is shown in Fig. 7

(6)

where and can be approximated by the slope of the line

in performance curves in Fig. 7(a) and (b), respectively.

III. SIMULATION

To demonstrate the proposed self-healing methodology,

SiGe HBT and CMOS RF LNAs have been designed in com-

mercially-available 6 metal layers, 0.18 m, 120 GHz SiGe

BiCMOS platform. For these designs, the actual simulation

models provided by the foundry are used. In addition, each

phase-shifter, RF switches and RF LNA were subjected to the

same process variations using Monte Carlo analysis and for

this simulation; models as provided by the foundry were again

used.

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Fig. 8. Simulation setup of the proposed methodology for healing SiGe LNA.(a) Self-healing SiGe RF LNA modeling. (b) RF system modeling.

A. Self-Healing SiGe HBT RF LNA

In this 2.80 GHz RF LNA ( 22.09 dB, dB

19.81 dBm), two tuning knobs have been introduced, one to

change the bias current and other to change the capaci-

tance in the LC tank. The change in the capacitance is achieved

by changing the voltage on the varactor, . Fig. 8(a) shows

the simulation setup in whichthe feedback is applied to the LNA

using an on-chip phase shifter such that the LNA becomes un-

stable and begins oscillations. Thisenables the self generation of

a test signature without any input test stimulus. In this test-setup

the phase-shifter is designed such that no oscillation occurs for

catastrophic failures while oscillation frequency shifts for the

parametric failures around the response of the golden LNA.For illustration, 10 single-catastrophic failures (5 open and 5

short) were introduced at 5 nodes [see Fig. 8(a)] of the LNA

and 100 parametric instances of the LNA were generated using

Monte Carlo simulations. Simulations were performed in Ca-

dence Spectra using Advance Design System (ADS) dynamic

link tools and the sinusoidal signal at the output of the LNA

was down-converted using a behavioral model of a RF-mixer,

a low-pass filter and a band-pass filter in MATLAB, as shown in

Fig. 8(b). In addition, all DSP processing was implemented in

MATLAB.

The down-converted oscillation frequency of all the 110 sam-

ples of the LNA (with 100 parametric and 10 catastrophic de-

fects) is shown in Fig. 9. It is can be inferred from the Fig. 9

that for some of the LNAs no-oscillations have occurred, while

Fig. 9. Oscillation frequency of the various LNA samples.

for others the oscillation frequency is around the response of the

golden LNA. It is important to note that no oscillation has oc-

curred for the LNA with catastrophic defects. Thus, depending

on the occurrence of the oscillation, the LNA with parametric

variations can be easily separated from LNAs with catastrophic

defects.

To determine which samples of the LNAs should be chosen

for the self-calibration/correction, using MARS, a nonlinear

prediction model to predict the forward small signal transmis-

sion gain (Gain, dB) and 1-dB compression point (P1 dB, dBm)

was developed from the response of the training set, which had

300 LNAs. These 300 LNAs were obtained by Monte Carlo

simulation. The prediction model was used to find the specifi-

cations and the predicted gain and P1 dB of the 100 samples of

the LNA is shown in Fig. 10(a) and (b), respectively.

It is can be seen from the results shown in Fig. 10 that the

predicted performance of the LNA from the self-generated testsignature is quite accurate. Thus, this methodology can be used

for on-chip self-testing of the LNAs. Let us assume that the al-

lowable performance specification of the gain is from 21.0 to

23.0 dB and P1 dB should be greater than 21.0 dBm. Consid-

ering an error in the prediction at the boundary of the allowable

specifications, 29 samples were selected for calibration/correc-

tion to meet the gain specification and 21 samples to meet P1 dB

specification. While selecting these samples for calibration/cor-

rection, acceptable band for gain specification was tighten from

21.20 to 22.80 dB and acceptable P1 dB specification was as-

sumed to be greater than 20.80 dBm.

The self-calibration/correction was performed with the helpof two tuning knobs. The first tuning knob (Knob1) changes the

varactor voltage in the LC tank of the LNA, while the

second tuning knob (Knob2) changes the bias current of

the LNA (see Fig. 8). The performance curves of the LNA with

respect to these two tuning knobs, Knob1 and Knob2, are shown

in Fig. 11(a) and(b). The obtained equations are

(7)

(8)

where in dB, in dBm, in A, and

in V.

For yield improvement, the change in tuning knobs was cal-

culated using above equations and the predicted value of the

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Fig. 10. SiGe RF amplifier analysis of parametric failures. (a) Prediction ofgain of SiGe RF amplifier. (b) Prediction of P1 dB of SiGe RF amplifier.

Fig. 11. Performance curves for 2.8 GHz SiGe RF amplifier. (a) Performance

curves: P1 dB of SiGe LNA as a function of the calibration/ tuning knobs.(b) Performance curves: gain of the SiGe LNA as a function of the calibra-tion/tuning knobs.

specifications from the nonlinear prediction model was used to

calculate and P1 dB. To illustrate, in this experiment

it is assumed that the desired value of P1 dB for the cali-

brated/corrected LNAs should be 20.5 dBm. Also, lower de-

sired value of gain should be 21.4 dB and upper desired value

of gain should be 22.6 dB. Thus, in (7) for all the LNAs which

do not meet lower specification of the gain, the left-hand side is

given by predicted value, while LNAs which do

not meet upper specification of gain, the left hand side is given

by predicted value. Also, in (8) for all LNAs,

Fig. 12. Specification distribution before and after healing of SiGe LNA usingKnob2.

TABLE ISUMMARY OF HEALING RESULTS OF SIGE LNA (KNOB 2)

dB predicted value. For all of these calibra-

tions/corrections, is in between 1.31 dB to 1.12 dB and

maximum dB is 1.08 dBm. Also, the is limited

in the range of to 2.25 V and the change in is below

12.10 A.1) SiGe LNA Calibration Using a Single Knob for P1 dB: In

this subsection, the proposed calibration procedure was applied

to 21 samples of the LNA to increase their P1 dB above 21.0

dBm. Only onetuning knob, Knob2, was used. Thespecification

distribution before and after healing is given in Fig. 12 and the

summary of the results is given in Table I. It can be inferred

from the results obtained that all of the LNAs began meeting

the P1 dB specification. However, the distribution for gain has

not improved much. To increase the number of LNA samples

with an allowable tolerance of the gain, the calibration for the

gain is explored in the next subsection.

2) SiGe LNA Calibration Using a Single Knob for Gain: Inthis subsection, the SiGe LNA samples were calibrated using

Knob1, with focus on improving the yield of gain. The obtained

results are shown in Fig. 13 and are summarized in Table II.

The above results show an improvement in gain yield to 97%,

but P1 dB yield only to 91%. Hence, to get better yield of the

LNA, calibration was performed using both of the knobs.

3) SiGe LNA Calibration Using Both the Knobs: In this cal-

ibration procedure both knobs were used and obtained results

are shown in Fig. 14 and summarized in Table III. Samples

which did not meet both gain and P1 dB specifications were

calibrated using both the knobs, and the change in the calibra-

tion knobs was calculated by solving two equations

, dB

). Samples which were not meeting P1 dB alone

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Fig. 13. Specification distribution before and after healing of SiGe LNA usingKnob1.

TABLE IISUMMARY OF HEALING RESULTS OF SIGE LNA (KNOBS 1)

Fig. 14. Specification distribution before and after healing of SiGe LNA usingboth Knob1 and Knob2.

TABLE IIISUMMARY OF HEALING RESULTS OF SIGE LNA (BOTH KNOBS)

were calibrated using Knob2, and Knob1 was used for cali-

brating samples which were not meeting gain alone.

Based on above the results it can be concluded that the

yield of the LNA has increased by the proposed self-healing

methodology, which includes self-testing and self-calibration/

correction.

Fig. 15. Self-healing CMOS RF LNA modeling.

B. Self-Healing CMOS RF LNA

To further demonstrate the self-healing methodology, a 1.85

GHz CMOS LNA was designed which a 16.80 dB,and dB 10.71 dBm. In this LNA, as shown in Fig. 15,

two tuning knobs have been introduced, one to change the bias

voltage and the other to change the power supply .

Again, in this design, the phase-shifter is designed such that no

oscillation occurs for catastrophic failures, while the oscillation

frequency shifts for the parametric failures around the response

of the golden LNA. For illustration, 10 single-catastrophic fail-

ures (5 open and 5 short) were introduced at 5 nodes of the LNA

(see Fig. 15) and 100 parametric instances of the LNAwere gen-

erated by Monte Carlo simulations. Simulations wereperformed

in Cadence Spectra using ADS dynamic link.

Similar to the results of the previous section, the analysis

showed that for the LNAs with catastrophic failures no oscilla-tions occurred, while for others the oscillation frequency was

around the response of the golden LNA. Thus, depending on

the occurrence of the oscillation, the LNA with parametric vari-

ations can be separated from LNAs with catastrophic defects.

To determine which samples of LNAs should be selected for

self-calibration/correction, a nonlinear prediction model using

MARS was developed to predict the forward transmission gain

and P1 dB. The prediction model was developed using the

training set which had 300 LNAs, which were again obtained

using Monte Carlo simulations. The gain and P1 dB specifi-

cation was predicted using the nonlinear prediction model and

self-calibration/correction was performed by the calibrationproduce described in the previous sections.

The self-calibration/correction was performed with the help

of the two tuning knobs designed for the LNA. The perfor-

mance curves of the LNA with respect to these two tuning

knobs, Knob1 and Knob2, are shown in Fig. 16. The obtained

equations of the form of (5) are

(9)

dB (10)

where in dB, 1 dB in dBm, , in V, and in V.

Similar to the pervious subsection, for yield improvement,

above equations were used and 29 samples were selected forcal-

ibration to meet the gain specification and 28 samples to meet

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Fig. 16. Performance curvesfor 1.85 GHz CMOS LNA. (a) Gainof the CMOSLNA as a function of the tuning knobs. (b) P1 dB of the CMOS LNA as afunction of the tuning knobs.

Fig. 17. Specification distribution of CMOS LNA before and after healingusing Knob1.

the P1 dB specification. In this experiment, it is assumed that

that the allowable performance specification of gain is from

15.20 to 17.80 dB and P1 dB should be greater than 11.40

dBm. To select LNAs for calibration/correction purposes, the

acceptable gain was tightened in the range from 15.65 dB to

17.40 dB and P1 dB was greater than 10.80 dBm. During

tunable-knob value selection using (9) and (10), the desired

value of P1 dB was 10.65 dBm, while lower desired value

of gain was 15.70 dB and upper desired value of gain was

17.25 dB. The obtained results of the calibration/correction are

shown below. For all these calibrations/correction, is in

between 1.03 dB to 1.77 dB and maximum P1 dB is 2.01dBm. Also, the change in bias voltage is in the range of

0.072 to 0.154 V and the change in power supply is

below 0.44 V.

1) CMOS LNA Calibration Using a Single Knob for P1 dB:

In this sub-section, the proposed calibration procedure was ap-

plied to 28 samples of the CMOS LNA to increase their P1 dB

above 11.40 dBm. Only one calibration knob, Knob1, was

used. The summary of specification distribution before and after

calibration is shown in Fig. 17 and given in Table IV.

The obtained results show that after calibration only 2% of the

samples are not meeting the P1 dB specification. However, the

distribution for the gain has not improved. To increase number

of LNA samples with in allowable tolerance of the gain, the

calibration for the gain is explored in the next subsection.

TABLE IVSUMMARY OF HEALING RESULTS OF CMOS LNA (Knob 1)

TABLE VSUMMARY OF HEALING RESULTS OF CMOS LNA (Knob2)

Fig. 18. Specification distribution of CMOS LNA before and after healingusing Knob2.

2) CMOS LNA Calibration Using a Single Knob for Gain: In

this subsection, the LNA samples were calibrated using Knob2

with focus on improving yield of the gain. The obtained results

are summarized in Table V and shown in Fig. 18.

The above results show improvement in gain yield to 96%,

but the P1 dB yield is still 91%. Hence, to obtain better yield

for the LNA, calibration was performed using both the knobs.

3) CMOS LNA Calibration Using Both the Knobs: In this

calibration procedure both knobs were used and the obtained re-

sults are given in Table VI and shown in Fig. 19. Samples whichwere not meeting both gain and P1 dB specifications were cal-

ibrated/corrected using both knobs and the change in the cal-

ibration knobs was calculated by solving (9) and (10). Sam-

ples which were not meeting P1 dB alone were calibrated using

Knob1, and Knob2 was used for calibrating samples which were

not meeting the gain alone.

Based on the results shown in Table VI, it can be concluded

that the yield of theCMOSRF amplifier has increased from 81%

to 94% by the proposed self-healing methodology.

IV. HARDWARE PROTOTYPE

As a proof of concept, a hardware prototype of the proposed

self-healing RF amplifier has been made using commercially

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Fig. 19. Specification distribution of CMOS LNA before and after healingusing both Knobs.

TABLE VISUMMARY OF HEALING RESULTS OF CMOS LNA (BOTH KNOBS)

Fig. 20. Hardware prototype of the proposed self-healing RF amplifier.

available LNA, as shown in Fig. 20. To change the performance

of the LNA, a single tuning knob that varies the power supply

was used. Three samples of the LNA were considered and

it was assumed that the golden sample was LNA1. In addition,it is assumed that the allowable performance specification of

the gain (@875 MHz) is from 10.50 to 12.50 dB. The perfor-

mance curve of the LNA (Gain versus ) is shown in Fig. 23.

The output oscillation frequency of the hardware prototype is

shown in Fig. 21 at 3.5 V. The measured gain for LNA3

is 10.43 dB, LNA2 is 11.77 dB, and LNA1 is 11.56 dB. The

measured gain of the LNAs versus output oscillation frequency

of LNAs in the feedback mode after down-conversion is shown

in Fig. 22. It can be inferred from these measurement results

that the oscillation frequency of the proposed self-healing LNA

changes with the performance of the LNA.

Let us assume that the prediction model to predict the gain of

the LNAs is made as explained in Section II and the predicted

gain for LNA2 and LNA3 is 11.00 and 10.00 dB, respectively.

Fig. 21. Spectrum of the output after the feedback loop and after the downconversion.

Fig. 22. Gain versus down-converted oscillation frequency of the LNA withfeedback.

Fig. 23. Performance curve of a hardware prototype: Gain of LNA1 as a func-tion of the tuning knob.

Hence, by the proposed methodology, the power supply

voltage that needs to be changed will be given by

(11)

where (see Fig. 23). Let the target value of the gain for

which both the LNAs need to be calibrated be 11.5 dB. Then

tuning knob for LNA2 should be changed by 0.5 V and for

LNA3 should be changed by 1.5 V.

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TABLE VIISUMMARY OF CALIBRATION RESULTS OF A HARDWARE PROTOTYPE

Fig. 24. Chip prototype of the self-healing X-band SiGe LNA with self-gener-ated test signature.

After tuning/calibration, the gain of LNA2 was measured to

be 12.25 dB and that of LNA3 was measured to be 11.49 dB (see

Table VII), which are in range of allowed values of the LNAs

gain. Hence, this experiment demonstrates that by tuning this

self-healing LNA, the performance of the LNA can be changed

and thus it can be used to increase their manufacturing yield.

V. CHIP PROTOTYPE

A. Simulation Results

To further demonstrate our self-healing methodology con-

cept, a cascode X-band SiGe LNA was designed as a chip pro-

totype. In this SiGe LNA, small-signal gain is the targeted spec-

ification to be self-healed. The LNA has a gain of 15.00 dB at

9 GHz. To achieve self-healing, an on-chip phase-shifter is de-

signed as shown in Fig. 24 and the bias current is used as

a tuning knob for calibration purposes. The die size of the LNAis 1.1 m 0.8 m and the overhead of the additional required

circuitry for self-healing is around 0.4 m 0.275 m, which is

about 12% of the total area. In this calculation, area of RF and

DC pads are not included.

To demonstrate the self-healing methodology, 100 samples of

the LNA were obtained using Monte Carlo simulations which

include process variations in both RF amplifier as well as in

extra circuitry (RF switches and phase-shifter). It is assumed

that the allowable range in gain variation is from 13.50 to 16.50

dB. To show that yield improvement is possible, as described

in the previous sections, the following steps were followed. It

is important to note that for this experiment the post layout ex-

tracted netlist of SiGe LNA was used. Thus, this experiment is

very close to the healing of actual ICs.

Step 1) LNA was forced to oscillate by providing feedback

through the phase shifter. The phase shifter was

made a part of the feedback loop by changing the

voltage from 0.0 to 3.3 V at self-healing control

knob, Vself (see Fig. 24). This completes the feed-

back and the LNA begins oscillating, thereby gen-

erating a sinusoidal RF test signal output, as shown

in Fig. 24.

Step 2) This self-generated test signature is used to assess

the impact of process variations on the performance

of the LNA. The prediction of gain was performed

using a nonlinear prediction model similar to that

described in the Sections II and III. The nonlinear

prediction model was developed using MARS and

response of 300 samples of LNA was used to de-

velop this model. Again, these 300 samples were ob-

tained using Monte Carlo simulations.

Step 3) To determine which samples of the LNAs should be

chosen for self-calibration/correction, the gain of the

LNAs was predicted using a nonlinear predictionmodel (similar to that in Sections II and III). Con-

sidering an error in the prediction at the boundary of

the allowable gain, the range of allowable gain was

tightened from 13.75 to 16.25 dB and then 16 sam-

ples were selected for self-calibration/correction.

Step 4) The performance curve (Gain versus ) was used

to obtain the dependency between gain and the

tuning knob and the resulting equation of the

form of (5) is

(12)

where in dB and is in .

Step 5) Similar to the previous sections, for yield improve-

ment, the change in the tuning knob (bias current)

was calculated using (12). The predicted value

of the specifications from the nonlinear predic-

tion model was used to calculate . The lower

desired value of gain was 14.10 dB and upper

desired value of gain was 15.90 dB. Thus, in (12)

for all the LNAs which do not meet lower spec-

ification of the gain, the left-hand side is given

by predicted value, while LNAs

which do not meet upper specification of gain, the

left-hand side is given by predicted

value. Subsequently, the tuning knob value was

adjusted and the yield improvement, as shown in

Fig. 25 was achieved.

Based on the results shown above in Fig. 25, it can be con-

cluded that the yield of this X-band amplifier has increased from

87% to 97% by the proposed self-healing methodology.

B. Measurement Results

To demonstrate the self-healing mode, the measurement setup

as shown in Fig. 26 was used. In this setup, no RF input was

applied to the RF LNA and only one RF probe was used for

sensing self-generated sinusoidal RF signal. The RF sinusoidal

signal was generated when a voltage 3.3 V was applied at

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Fig. 25. Improvement in the yield of X-band SiGe self-healing LNA.

Fig. 26. Measurement results and the measurement setup of the X-band SiGeLNA in self-healing mode.

self-healing control (Vself), The self-generated sinusoidal signal

of 8.0 GHz from the RF amplifier is shown in Fig. 26. The de-

viation in the measured oscillation frequency from that of sim-

ulated value of 9.0 GHz, is mainly because of the shift in the

performance of the amplifi

er versus simulations, as shown inFig. 27. We have noticed similar gain reduction/variation in the

other designs sent for this fabrication run as well. To demon-

strate that as the performance of the RF amplifier changes, the

oscillation frequency of the amplifier in the self-healing mode

also changes, the measurement results of three RF amplifiers

is plotted in Fig. 28, which shows their gain versus oscillation

frequency of the self-generated signal. The gain of the RF am-

plifier was measured using a conventional VNA setup. During

this VNA setup, the self-healing control (Vself) was equal to

0 V and the RF LNAs were stable (the measured stability fac-

tors [21], and , are greater than 1 and no oscillations

were observed). To demonstrate the calibration capability in this

RF LNA, the measured performance curve of the RF amplifier

(Gain versus ) is shown in Fig. 29.

Fig. 27. Measured and simulated gain of the X-band SiGe LNA. Please notethat during this Gain simulation and measurement, the RF LNA was not in self-healing mode. The feedback was switched off as control signal, Vself, was equalto 0 V.

Fig. 28. Measured gain of the X-band SiGe LNA and the frequency of theself-generated sinusoidal signal from the LNA under self-healing mode.

Fig. 29. Performance curve of X-band SiGe LNA: Gain of Sample 2 as a func-tion of the tuning knob .

This experiment demonstrates that it is possible to generate

on-chip oscillations by providing on-chip feedback to the RF

amplifier. In addition, as RF amplifiers performance varies,

the oscillation frequency also varies. Further, the performance

of the amplifier can be changed by designing tuning knob in

the amplifier as shown in the performance curve (see Fig. 29).

Thus, by developing nonlinear prediction/regression model

(such as using MARS) from large set of samples, the manu-

facturing yield of the RF amplifier can be increased using the

performance curves and equations of the form (5), as explained

in the previous sections.

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TABLE VIIISUMMARY OF HEALING USING PHASE-SHIFTER VARIATION

VI. DISCUSSION

It can be inferred from the above results that the proposedmethodology provides yield improvement; the trade-off is the

extra area required for implementing the RF switches, the phase-

shifter and the tuning knobs. In addition to the extra area, at

RF frequencies loading effects of this extra circuitry need to be

taken into consideration in the RF design. The SiGe 2.8 GHz

RF of Section III has a gain of 22.17 dB and P1 dB of 19.93

dBm without feedback (without RF switch and phase shifter).

In contrast, it has a gain of 22.09 dB and P1 dB of 19.81 dBm

after adding the required extra circuitry. This shows that perfor-

mance of RF amplifier changes due to the extra circuitry, but it

is important to note that the change in performance is not very

large and it can overcome with some extra design effort.In our proposed methodology, the on-chip phase shifter

should be robust to manufacturing process variations such that

the effect of the process variations on the performance of the

on-chip phase-shifter should not dominate as compared to the

performance of the RF amplifier. In our experiments, the phase

shifter was designed such that the effect of process variation

on the yield recovery of RF amplifiers (SiGe RF amplifier and

CMOS RF amplifier) is not affected because of the manufac-

turing process variations in the phase shifter. To illustrate this,

when ideal phase-shifter was used and the proposed self-healing

methodology was applied for healing SiGe LNA for gain using

Knob 2 (Section III, Part A, SiGe LNA Calibration using aSingle Knob for Gain), after self-healing procedure, the

of LANs with gain in the range from 21.0 to 23.0 dB was

97%. This result is same as the yield recovery presented in

this paper when phase-shifter was subjected to manufacturing

process variations. The summary of yield recovery is shown in

Table VIII. Similar experiment was performed during healing

of CMOS RF LNA (Section III, Part B, CMOS LNA Calibra-

tion using a Single Knob for Gain) where yield recovery was

not effected because of on-chip implementation of phase-shifter

that was subjected to process variations. These experiments

show that with proper design of phase sifter, implementing

on-chip phase shifter for yield recovery is possible.

In addition, while using active tuning knobs (changing bias

voltage/current), the average power consumption can change.

Fig. 30. Power distribution before and after self healing of X-band SiGe LNA(see Section V).

TABLE IXPOWER CONSUMPTION MATRIX BEFORE AND AFTER HEALING OF X-BAND

SIGE HBT LNA (SECTION V)

To illustrate the change in profile of power consumption, the

power consumption of the X-Band SiGe RF amplifier (see

Section V) before and after healing is shown in Fig. 30 and

results are summarized in Table IX. The mean of the power con-

sumption is almost the same before and after self-healing/yield

improvement because after healing some of the RF amplifiers

started consuming less power while other started consuming

more power. From these results, it can be concluded that the

average power consumption change is very small, but it should

be accounted for in certain low-power applications.

VII. SUMMARY

In this paper, a new self-healing methodology has been

proposed for RF amplifier circuits. The methodology has been

demonstrated on an embedded RF LNA in the RF front-end

systems. It has been shown through simulations that a signif-

icant yield improvement of the RF amplifier can be achieved

by using this methodology. Both a board-level prototype and

a chip-level prototype of the self-healing LNA have also been

demonstrated. Results from the experiments performed are

quite encouraging and it can be concluded that the proposed

methodology can be considered an alternate solution for the

development of future self-healing systems.

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[3] T. Najibi, How Designers can Increase Parametric Yield, EE Times,Nov. 21, 2003 [Online]. Available: http://www.eetimes.com

[4] ITRS, 2007 Edition of the International Technology Roadmap forSemiconductors (ITRS), 2007.

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[6] T. Das, A. Gopalan, C. Washburn, and P. R. Mukund, Self-calibrationof input-match in RF front-end circuitry, IEEE Trans. Circuits Syst.,vol. 52, no. 12, pp. 821825, Dec. 2005.

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[7] D. Han, B. S. Kim, and A. Chatterjee, DSP driven self-tuning ofRF circuits for process-induced performance variability, IEEE Trans.Very Large Scale Integr. (VLSI) Syst., vol. 18, no. 2, pp. 305314, Feb.2010.

[8] K. Jayaraman, Q. Khan, B. Chi, W. Beattie, Z. Wang, and P. Chiang,A self-healing 2.4 GHz LNA with on-chip S11/S21 measurement/cal-ibration for in-situ PVT compensation, in Proc. IEEE Radio Freq. In-tegr. Circuits Symp. (RFIC), 2010, pp. 311314.

[9] K. Arabi and B. Kaminska, Oscillation-test methodology for low-costtesting of active analog filters, IEEE Trans. Instrum. Meas., vol. 48,no. 4, pp. 798806, Aug. 1999.

[10] K. Arabi and B. Kaminska, Testing analog and mixed-signal inte-grated circuits using oscillation-test method, IEEE Trans. Comput.-

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[13] A. Goyal, M. Swaminathan, and A. Chatterjee, Low-cost pecificationbased testing of RF amplifier circuits using oscillation principles, J.Electron. Test. Theory Appl. (JETTA), vol. 26, no. 1, pp. 1324, Feb.

2010.[14] A. Goyal, M. Swaminathan, and A. Chatterjee, A novel self-healing

methodology for RF amplifier circuits based on oscillation principles,Proc. IEEE Design Autom. Test Euro., pp. 16561661, 2009.

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[16] J. H. Friedman, Multivariate adaptive regression splines, The Annalsof Statistics, vol. 19, no. 1, pp. 1141, 1991.

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Abhilash Goyal (S09) received the Bachelor ofTechnology (Honors) degree in electronics andelectrical communication engineering from IndianInstitute of Technology (IIT), Kharagpur, India,and the Masters and Ph.D. degrees in electricaland computer engineering from Georgia Institute ofTechnology, Atlanta, (Georgia Tech).

During his Ph.D. at Georgia Tech, he was involvedas a mentor for undergraduate research programat the Electrical and Computer Engineering (ECE)School and taught (instructor) ECE3710 course at

ECE School. He has 15 publications in refereed journals and conferenceswith four patents/invention disclosures. He serves as a Technical CommitteeMember for IEEE Students Technology Symposium (TechSym), IEEEElectrical Design of Advanced Packaging and Systems (EDAPS) and IEEEElectronic Components and Technology Conference (ECTC). His research

interests include RF passive/active circuits design and test, IC/board-levelpackaging, mixed-signal/digital design and test, self-correctable/healingmixed-signal/RF systems.

Madhavan Swaminathan (M95SM98F06) re-ceived the B.E. degree in electronics and communi-cation from the University of Madras, Madras, India,and the M.S. and Ph.D. degrees in electrical engi-neering from Syracuse University, Syracuse, NY.

He is currently the Joseph M. Pettit Professorin Electronics with the School of Electrical andComputer Engineering and the Director of theInterconnect and Packaging Center (IPC), an SRCCenter of Excellence, Georgia Institute of Tech-nology (Georgia Tech). He was the Deputy Director

of the Packaging Research Center, Georgia Tech from 20042008. He is theco-founder of Jacket Micro Devices, a company specializing in integrateddevices and modules for wireless applications (acquired by AVX Corp.) andthe founder of E-System Design, an EDA company focusing on CAD solutionsfor integrated microsystems, where he serves as the CTO. Prior to joiningGeorgia Tech, he was with the Advanced Packaging Laboratory, IBM workingon packaging for super computers. He has over 325 publications in refereedjournals and conferences, has co-authored 3 book chapters, has 22 issuedpatents, and has several patents pending. While at IBM, he reached the secondinvention plateau. He served as the Co-Chair for the 1998 and 1999 IEEETopical Meeting on Electrical Performance of Electronic Packaging (EPEP),served as the Technical and General Chair for the IMAPS Next GenerationIC & Package Design Workshop, serves as the Chair of TC-12, the TechnicalCommittee on Electrical Design, Modeling and Simulation within the IEEE

CPMT Society and was the Co-Chair for the 2001 IEEE Future Directions inIC and Package Design Workshop. He is the co-founder of the IMAPS NextGeneration IC and Package Design Workshop and the IEEE Future Directionsin IC and Package Design Workshop. He is the author of the book PowerIntegrity Modeling and Design for Semiconductors and Systems, PrenticeHall, 2007 and co-editor of the book Introduction to System on Package(SOP), McGraw Hill, 2008. He is currently a visiting professor at ShanghaiJiao Tong University, Shanghai, China and Thiagarajar Engineering College,Madurai, India. His research interests include mixed signal micro-system andnano-system integration with emphasis on design, CAD, electrical test, thermalmanagement and new architectures.

Dr. Swaminathan also serves/served on the technical program committeesof EPEP, Signal Propagation on Interconnects workshop, Solid State Devicesand Materials Conference (SSDM), Electronic Components and TechnologyConference (ECTC), International Symposium on Quality Electronic Design(ISQED) and MTT-1 committee on CAD. He is the founder of ElectricalDesign of Advanced Packaging and Systems (EDAPS), a Signal IntegritySymposium in the Asian region. He has been a guest editor for the IEEETRANSACTIONS ON ADVANCED PACKAGING and IEEE TRANSACTIONS ONMICROWAVE THEORY AND TECHNIQUES. He was the Associate Editor of theIEEE TRANSACTIONS ON COMPONENTS AND PACKAGING TECHNOLOGIES. Hewas a recipient of the 2002 Outstanding Graduate Research Advisor Awardfrom the School of Electrical and Computer Engineering, Georgia Tech, andthe 2003 Outstanding Faculty Leadership Award for the mentoring of graduateresearch assistants from Georgia Tech. He is also the recipient of the 2003Presidential Special Recognition Award from IEEE CPMT Society for hisleadership of TC-12 and the IBM Faculty Award in 2004 and 2005. He hasalso served as the co-author and advisor for a number of outstanding studentpaper aw ards at EPEP 00, EPEP 02, EPEP 03, EPE P 04, EPEP 08, ECTC08, ECTC 08, APMC 05, and the 1997 IMAPS Education Award. He is alsothe recipient of the Shri. Mukhopadyay Best Paper Award at the InternationalConference on Electromagnetic Interference and Compatibility (INCEMIC),Chennai, India, 2003, the 2004 Best Paper Award in the IEEE T RANSACTIONS

ON ADVANCED PACKAGING, the 2004 Commendable Paper Award in the IEEETRANSACTIONS ON ADVANCED PACKAGING and the Best Poster Paper Awardat ECTC 04 and 06. In 2007, he was recognized for his research through theTechnical Excellence Award given by Semiconductor Research Corporation(SRC) and Global Research Corporation (GRC).

Abhijit Chatterjee (F07) received the Ph.D.degree in electrical and computer engineering fromthe University of Illinois at Urbana-Champaign,Urbana-Champaign, in 1990.

He is a Professor with the School of Electricaland Computer Engineering, Georgia Institute ofTechnology (Georgia Tech), Atlanta. In 1995, hewas a Collaborating Partner in NASAs New Millen-nium project. He has published over 350 papers inrefereed journals and meetings and holds 12 patents.

He is a co-founder of Ardext Technologies Inc.,a mixed-signal test solutions company and served as Chairman and ChiefScientist from 20002002. He is currently directing research at Georgia Tech

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in mixed-signal/RF design and test funded by NSF, SRC, MARCO-DARPAand industry.

Dr. Chatterjee was a recipient of the NSF Research Initiation Award in 1993and the NSF CAREER Award in 1995. He has received four Best Paper Awardsand three Best Paper Award nominations. In 1996, he received the OutstandingFaculty for Research Award from the Georgia Tech Packaging Research Center,and in 2000, he received the Outstanding Faculty for Technology TransferAward, also given by the Packaging Research Center.

Duane C. Howard (S09) received the B.S. degreein electrical engineering from Howard University,Washington, DC, in 2005. He is currently pursuingthe Ph.D. degree in electrical engineering from theGeorgia Institute of Technology, Atlanta.

In the summer of 2008, he worked as a RF andMicrowave Design Engineer with Texas Instruments,Dallas, where he worked on developing meta-mate-rials for interconnects and antennas. In the summerof 2010, he worked as an RF Engineer with TexasInstruments, Baltimore, MD, where he designed am-

plifiers for low power transceiver applications. His current research interestsinclude millimeter wave circuits, reconfigurable circuits, and on chip testing.

Mr. Howard was the recipient of the 2008 Texas Instruments Focus Schol-

arship and the 20102011 Georgia Tech Ivan Allen School of International Af-fairs, Sam Nunn Science Technology and National Security Fellowship.

John D. Cressler (F01) received the Ph.D. degreefrom Columbia University, New York, NY, in 1990.

He was at IBM Research from 1984 to 1992, andon the faculty of Auburn University from 1992 to2002. Since 2002, he has been on the faculty ofGeorgia Institute of Technology (Georgia Tech), At-lanta, where he is currently Ken Byers Professor ofElectrical and Computer Engineering. His researchinterests include Si-based (SiGe/strained-Si) het-erostructure devices and technology, mixed-signalcircuits built from these devices, radiation effects,

cryogenic electronics, device-to-circuit interactions, noise and reliabilityphysics, device-level simulation, and compact circuit modeling. He has pub-lished over 500 scientific papers related to his research and is the co-author ofSilicon-Germanium Heterojunction Bipolar Transistors (Artech House, 2003),the author of Reinventing Teenagers: the Gentle Art of Instilling Characterin Our Young People (Xlibris, 2004), the editor of Silicon Heterostructure

Handbook: M aterials, Fa brication, Devices , Circuits, and Applications of SiGe

and Si Strained-Layer Epitaxy (CRC Press, 2006), and the author of SiliconEarth: Introduction to the Microelectronics and Nanotechnology Revolution

(Cambridge Univ. Press, 2009). During his academic career he has graduated30 Ph.D. students and 30 M.S. students.

Dr. Cressler has served as Associate Editor for the IEEE JOURNAL OF SOLID-STATE CIRCUITS,theIEEETRANSACTIONS ONNUCLEARSCIENCE, and the IEEETRANSACTIONS ON ELECTRON DEVICES. He has been active on numerous con-

ference program committees, including as the Technical Program Chair of the1998 ISSCC, the 2007 NSREC, and the 2011 BCTM. He was a recipient of anumber of awards for both his teaching and research.

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