Pete Zampardi, Dave Nelson, Steve Rohlfing Application of a Design of Experiment Approach to Handset Power Amplifier Design Pete Zampardi, Dave Nelson, Steve Rohlfing

DOE Gets a Bad Rap Why Does DOE Get Such a Bad Reputation? Bad implementation (we don’t advocate it where it doesn’t make sense) People using them as an excuse not to think (best DOEs are by people that understand the problem!) People try to use it for crank finding problems rather than crank turning problems (best for optimization, not innovation) Need a New Level Beyond Black-Belt. We propose “Ninja” that comes in and kicks the black-belts butt!

Used Correctly, It Makes Your Why Use DOE? MINIMIZES the number of variants necessary to evaluate how factors and their INTERACTIONS affect measured outcomes Make what you are already doing easier! Learn more in the process Provides systematic method for modeling these effects Example: PAE, GAIN, ACP vs. factors Reduce design time by providing designers with critical information for new projects (how much do you need to change something to meet new spec?) Do this on a REAL design. Have something improved when you are done! Used Correctly, It Makes Your Your Life Easier!

Make-up of Cellular Handset PA 9/12/2018 Make-up of Cellular Handset PA DOE Factors Input stage, Output stage, Bias circuits, Matching Cint Rb,out Ain Aout Re,in For a PA, the circuit is not very complicated Need to understand how different technology parameters affect the bias circuits, the power transistors, and the passives (matching) Must look at what each one requires separately then trade-off for best total design Tseng, UCLA Can be replaced With Tuner Can be replaced With Tuner Most of the RF Chain is a Prime Candidate for Using a Design of Experiments Approach!

What Factors Did We Pick? Minimize Number of On-wafer Variants (record is something like 78!) On-Chip Factors (27 Layouts) Input Stage Area (Ain) (+/- 25%) Output Stage Area (Aout) (+/-7%) Input Stage Ballast (emitter) (Re,in) (+/-50%) Output Stage Ballast (Rb,out) (+/- 33%) Inter-stage Cap Value (Cint) (+/- 20%) Off-Chip Factors (Not varied here) Vref Resistor (Icq’s) Input Match (Source pull) Output Match (Load pull) IMPORTANT CAVEAT NUMBER 1: Realistically, this was too many factors. because of uncontrolled variation (laminates, SMT, assembly), quite a few parts of each variant needed to be tested making this experiment more cumbersome than intended. to avoid this, use SAME panel for variants and SMTs from same batches KISS=Keep It Simple Stupid! Important Safety Tip - Egon Picked On-Chip Factors Designers Vary Most (or Asked About Most)

Test Results 5 Parts from each variant were tested (maybe not enough). We tested one subset of the DOE first (just as a check), then completed the full set Conditions Variants (same wafer, etc) IS-95 Modulation Temperature=25 C Vref=4.75 Volts Vbias=3.2 Volts Freq=836.5 MHz Other data (over temperature, modulation, freq, also available) IMPORTANT CAVEAT NUMBER 2: Analysis of DOE data requires single point values, but PA data is continuous. comparing at a single power, you may get screwed up someplace else. To deal with this, we defined new parameters that describe the curves for the data we were trying to fit.

Parameter Definitions Gain Slope, Gain Peak, PAE_14, PAE_peak, ACPR_belly, ACPR_MIN, ACP1_Slope, ACP2_Slope, ACP2_29

Summary of Behavior (for preceding data only) Gain PAE ACPR1 ACPR2 Cint   - 1st Area   2nd Area  None of the variables affected PAE much Larger Cint and 1st stage increase gain Larger Cint and 2nd stage improve ACPR1 and ACPR2 Without changing anything else, larger Cint improves Gain, PAE, and linearity for this circuit Score!

Data for All Variants IMPORTANT CAVEAT NUMBER 3&4: (3) Just because there is *Correlation* doesn’t mean it’s important. Need to check the range to see if you really care or can use it! (4) Make sure you vary stuff A LOT – Aout was space limited in this experiment

Position of belly affected Higher Lower Position of belly affected by A1 Depth of belly depends on Cint & A2

Cint and A2 improve ACPR1 (at 29 dBm) ACPR1 (29 dBm) Higher Lower Cint and A2 improve ACPR1 (at 29 dBm) bigger A1, R1, R2 degrade

ACPR2 (29 dBm) ACPR2 improved by Cint, A1, A2, R1 degraded by R2 Higher Lower ACPR2 improved by Cint, A1, A2, R1 degraded by R2

Summary of Effects (For This Design) Peak Gain Gain Slope PAE ACP Belly P_ACBelly ACP1 ACP1Slope ACP2 ACP2 Slope A1 ()   A2 () - R1 () R2 () Cint () Improves  Degrades

Design Figure: R1, R2, Cint Fixed Cint=Nominal If ACPR (belly) < ACPR_29 Part Fails Design contours can be generated from the equations/software

Summary A DOE design study was performed on a 29 dBm power amplifier. There were a LOT of interactions. Dependencies of PA performance parameters on RF chain parameters have been identified and should allow better optimization of these circuits (for performance margin, yield) This approach has since been applied to optimizing ballasting, stage sizes, and interstage matching. Also identified areas outside of die for improvement (laminate, SMT). Key Lessons Learned: Keep DOEs simple! Still effective since you can see interactions. Vary factors enough to impact stuff. Don’t be surprised other stuff you didn’t vary impacts results. Need some creativity in parameter definitions for analysis

THANK YOU!! Final Thoughts on DOE! Random Acts of Varying Stuff is NOT a DOE Best DOE’s Require Careful Thought “Designing” an Experiment Doesn’t Make It a DOE! DOE Follows a Certain Methodology THANK YOU!!