March 12, 2008Fan's MS Defense1 Soft Error Rate Determination for Nanometer CMOS VLSI Circuits Master’s Defense Fan Wang Department of Electrical and Computer Engineering Auburn University, AL USA Thesis Advisor: Dr. Vishwani D. Agrawal Thesis Committee: Dr. Fa Foster Dai and Dr. Victor P. Nelson

March 12, 2008Fan's MS Defense2 Outline  Background  Problem Statement  Contributions  Proposed soft error model  Proposed soft error propagation through logic  Experimental results  Discussion of results  Conclusion

March 12, 2008Fan's MS Defense3 Motivation for This Work  With the continuous downscaling of CMOS technologies, the device reliability has become a major bottleneck.  The sensitivity of electronic systems can potentially become a major cause of soft (non-permanent) failures.  The determination of soft error rate in logic circuits is a complex problem. There is no existing analysis method that comprehensively considers all the factors that influence the soft error rate.

March 12, 2008Fan's MS Defense4 Background  Certain behaviors in the state of the art electronic circuits caused by random factors.  Single event upset (SEU) is a non-permanent or transient error.  Definition from NASA Thesaurus: “ Single Event Upset (SEU): Radiation-induced errors in microelectronic circuits caused when charged particles [also, high energy particles] (usually from the radiation belts or from cosmic rays) lose energy by ionizing the medium through which they pass, leaving behind a wake of electron-hole pairs”.

March 12, 2008Fan's MS Defense5 What is Soft Error  A “fault” is the cause of errors. Faults can be permanent (hardware fault) or non-permanent.  A non-permanent fault is a non-destructive fault and falls into two categories:  Transient faults caused by environmental conditions like temperature, humidity, pressure, voltage, power supply, vibrations, fluctuations, electromagnetic interference, ground loops, cosmic rays and alpha particles.  Intermittent faults caused by non-environmental conditions like loose connections, aging components, critical timing, interconnect coupling, resistive or capacitive variations and noise in the system.  An error caused by a non-permanent fault is a “soft error”.  With advances in manufacturing, soft errors caused by cosmic rays and alpha particles remain the dominant causes of failures in electronic systems.

March 12, 2008Fan's MS Defense6 Soft Error Rate (SER) in Specific Applications  Figure of Merit: 1.Failures In Time (FIT): Number of failures per 10 9 device hours 2.MTTF (Mean Time To Failure): 1 year MTTF = 10 9 /(24*365) FIT = 114,155 FIT  SER of contemporary commercial chips is controlled to within 100~1000 FIT  Most hard failure mechanisms produce error rate on the order of 1~100 FIT  Programmable logic SER is almost 100 times larger than combinational logic

March 12, 2008Fan's MS Defense7 Soft Error Rate (SER) for SRAM-Based FPGA FPGA (Xilinx)XC4010EXC4010XL Process0.60μ0.35μ Vcc5V3.3V 1 SEU every1×10 6 hours2.8×10 5 hours M. Ohlsson, P. Dyreklev, K. Johansson and P. Alfke, “Neutron Single Event Upsets in SRAM-Based FPGAs,” Proc. IEEE Nuclear & Space Radiation Effects Conference, C. E. Stroud, “FPGA Architectures and Operation for Tolerating SEUs,” VLSI Design & Test Seminar, Auburn University, January 31,  Effects of smaller design rules and lower supply voltages  Radiation chamber measurement of SER at altitude of 10km at 60°N (Sweden): Projecting through 3 design rule shrinks and 2 voltage reductions we get ≈ 1 SEU every 28.2 hours

March 12, 2008Fan's MS Defense8 Reliability Requirements Year Density (megabit) Maximum data rate (MHz) MTTF (hours) FIT** 2.487x x x x10 5 Commodity flash memory reliability requirements* * from 2002 International Technology Roadmap for Semiconductors ITRS. ** FIT = 10 9 /MTTF

March 12, 2008Fan's MS Defense9 Single Event Transient (SET)  SET is caused by the generation of charge due to a high- energy particle passing through a sensitive node.  Each SET has its unique characteristics like polarity, waveform, amplitude, duration, etc., depending on particle impact location, particle energy, device technology, device supply voltage and output load.  An “off” transistor struck by a heavy ion with high enough LET* in the junction area is most sensitive to SEU.  Specifically, the channel region of an off-NMOS transistor and the drain region of an off-PMOS transistor are sensitive regions. * Linear Energy Transfer (LET) is a measure of the energy transferred to the device per unit length as an ionizing particle travels through material. Unit: MeV-cm 2 /mg.

March 12, 2008Fan's MS Defense10 Measured Environmental Data  Typical ground-level total neutron flux: 56.5cm -2 s -1.  J. F. Ziegler,.Terrestrial cosmic rays,. IBM Journal of Research and Development, vol. 40, no. 1, pp ,  Particle energy distribution at ground-level: “For both 0.5μm and 0.35μm CMOS technology at ground level, the largest population has an LET of 20 MeV-cm 2 /mg or less. Particles with energy greater than 30 MeV-cm 2 /mg are exceedingly rare.”  K. J. Hass and J. W. Ambles,.Single Event Transients in Deep Submicron CMOS, Proc. 42 nd Midwest Symposium on Circuits and Systems, vol. 1, Linear energy transfer (LET), MeV-cm 2 /mg Probability density

March 12, 2008Fan's MS Defense11 Details of SET Generation (a) Along the path traverses, the particle produces a dense radial distribution of electron-hole pairs. (b) Outside the depletion region the non-equilibrium charge distribution induces a temporary funnel-shaped potential distortion along the trajectory of the event (drift component). (c) Funnel collapses, diffusion component then dominates the collection process until all excess carriers have been collected, recombined, or diffused away from the junction area. (d) Current vs. Time to illustrate the charge collection and SET generation.

March 12, 2008Fan's MS Defense12 SET in CMOS Inverter *For example, in ami12 technology, when the output load capacitance is 100fF and the cumulative collected charge is 0.65pC, the amplitude of the voltage pulse is 0.65pC/100fF = 0.65 x C/100 x F = 0.65V.

March 12, 2008Fan's MS Defense13 Original Contributions of This Research

March 12, 2008Fan's MS Defense14 Problem Statement  Given background environment data  Neutron flux  Background LET distribution *Those two factors are location dependent.  Given circuit characteristics  Technology  Circuit netlist  Circuit node sensitive region data *Those three factors depend on the circuit.  Estimate neutron caused soft error rate in standard FIT units.

March 12, 2008Fan's MS Defense15 Proposed Soft Error Model  Single event effect exists as single event transient.  An SET has its unique characteristics like polarity, waveform, amplitude and duration.  Environmental neutrons come from cascaded interactions when galactic cosmic rays traverse earth’s atmosphere. Occurrence rate

March 12, 2008Fan's MS Defense16 Error Occurrence Rate  Environmental neutron flux is N/cm 2 -s, where N is the number of particles.  Each neutron particle bear different energy when it interacts with silicon.  Not all particles with enough energy will cause an error. There is some probability P per hit for a given particle energy. For a circuit node with sensitive region A (cm 2 ) and a given particle energy the SER probability per hit is P. If neutron flux rate is N/cm 2 -s, then the soft error occurrence rate at this node is (A x P x N)/s

March 12, 2008Fan's MS Defense17 Single Event Transient (SET)  For a circuit node a soft error occurs as a transient signal whose width depends on the energy of the striking neutron.  The transient width determines whether it can propagate through logic gates. Transient pulse width is the interval between Vdd/2 points.  The LET probability density function determines the transient width density statistics.  Typical charge collection depth L is 2μm for bulk silicon.  An ionizating particle with 1MeV-cm 2 /mg deposits about 10.8fC charge along each micron on its track. τ a is collection time constant and τ B is ion-track establishment time constant. Typical value for τ a and τ B is 1.64x and 5x respectively.

March 12, 2008Fan's MS Defense18 Summarizing  We model the soft error with two parameters:  Occurrence rate  Single event transient width  Next, we propose a propagation algorithm for the modeled soft error transient pulses.

March 12, 2008Fan's MS Defense19 Pulse Widths Probability Density Propagation X, Y are random variables X: input pulse width, Y : output pulse width f X (x): probability density function of X f Y (y): probability density function of Y Given function g: Y=g(X) Propagation function through a sensitized gate: g: Y=g{p: W/L, n:W/L, C load, technology} Assume: g is differentiable and an increasing function of X, so g’ and g -1 exist. Then, 1 X Y

March 12, 2008Fan's MS Defense20 Propagation Rule We use a linear “3-interval piecewise linear” propagation model to approximate the non-linear function g. Three-intervals: 1)Non-propagation, if D in ≤τ p. 2)Propagation with attenuation, ifτ p < D in < 2τ p. 3)Propagation with no attenuation, if D in  2τ p. Where  D in : input pulse width  D out : output pulse width  τ p : gate input output delay τpτp 2τp2τp 0D in = X D out = Y

March 12, 2008Fan's MS Defense21 Determination of Model Parameter We simulated a CMOS inverter using HSPICE This CMOS inverter is in TSMC035 technology, with nmos W/L ratio = 0.6µ/0.24µ and pmos W/L ratio = 1.08µ/0.24µ. The proposed 3-interval piecewise linear equation is approximated as

March 12, 2008Fan's MS Defense22 Pulse Width Density Propagation Through a CMOS Inverter

March 12, 2008Fan's MS Defense23 Validating Propagation Model Using HSPICE Simulation Simulation of a CMOS inverter in TSMC035 technology with load capacitance 10fF

March 12, 2008Fan's MS Defense24 Logic SEU Occurrence Rate Propagation Because all pulse widths are greater than or equal to 0, so we have: In f X (x) to f Y (y) conversion, there is a fraction of pulses being filtered out or attenuated due to electrical masking. We define electrical masking ration (EMR) as:

March 12, 2008Fan's MS Defense25 Soft error occurrence rate calculation for generic gate

March 12, 2008Fan's MS Defense26 Experimental Results for ISCAS85 Circuits  Assume probability of SEU per particle hit is  Assume the SET width density per circuit node follows normal distribution with mean µ = 150 and standard deviation σ = 50 for ground level environment.  At ground level, total neutron flux is 56.5 m -2 s -1.  Circuit are in TSMC035 technology and sensitive region per node is 10 µm 2.  For a circuit with n primary outputs and m nodes, we calculate the SER as:

March 12, 2008Fan's MS Defense27 SER Results on Workstation Sun Fire 280R Circuit#PIs#POs#GatesCPU s FIT/gate/ output C C C C C C C C

March 12, 2008Fan's MS Defense28 SER Results for Inverter Chains Circuit#PIs#POs#GatesCUP (s)FIT/gate Inv Inv Inv Inv Inv Inv

March 12, 2008Fan's MS Defense29 Methods Comparison Factors Considered LET Spec. Re-cov. Fanout Sensitive region Occurance rate Vectorsa pplied Location altitude Circuit Tech. SET degrad. Our work YesNoYes NoYes Rao et at. [1] YesNo Yes Rajaraman et al. [2] No YesNo Yes Asadi- Tahoori [3] No YesNo Zhang- Shanbhag[4] YesNoYes No Rejimon- Bhanja [5] No Yes No

March 12, 2008Fan's MS Defense30 Experimental Results Comparison Circuit # PI # PO # Gates Our approachRao et al. [1]Rajaraman et al[2] CPU s FITCPU sFIT CPU min. Error Prob. C x10 3 < x C x x C x x C x x Computing PlatformSun Fire 280RPentium 2.4 GHzSun Fire v210 Circuit TechnologyTSMC035Std µm70nm BPTM* AltitudeGround N/A *BPTM: Berkley Predictive Technology Model

March 12, 2008Fan's MS Defense31 More Result Comparison Measured Data Logic Circuit SER Estimation Ground Level Devices SER* (FIT/Mbit) Our WorkRao et al. [1] 0.13µ SRAMs [6] 10,000 to 100,000 1,000 to 10,0001x10 -5 to 8x10 -5 SRAMs, 0.25μ and below [7] 10,000 to 100,000 1 Gbit memory in 0.25µ [8] 4,200 * The altitude is not mentioned for these data.

March 12, 2008Fan's MS Defense32 Discussion  We take the energy of neutron to be the key factor to induce SEU. In real cases, there can also be secondary particles generated through interaction with neutrons.  Estimating sensitive regions in silicon is a hard task. Also, the polarity of SET should be taken into account.  Because on the earth surface, typical error rates are very small, their measurement is time consuming and can produce large discrepancy. This motivates the use of analytical methods. For example, a circuit may experience 1 SEU in 6 months (4320 hours), equals 231,480 FIT. It is also likely that the circuit has 0 SEU in these 6 months, so the measured SER is 0 FIT.

March 12, 2008Fan's MS Defense33  Fan-out stems should be considered. Two situations can arise:  When an SET goes through a large fan-out, the large load capacitance can eliminate the SET, or  If it is not canceled by the fan-out node, it will go through multiple fan-out paths to increase the SER.  It is highly recommended to have more field tests for logic circuits.  None of these SER approaches consider the process variation effects on SER. Discussion Continued

March 12, 2008Fan's MS Defense34 Conclusion  SER in logic and memory chips will continue to increase as devices become more sensitive to soft errors at sea level.  By modeling the soft errors by two parameters, the occurrence rate and single event transient pulse width density, we are able to effectively account for the electrical masking of circuit.  Our approach considers more factors and thus gives more realistic soft error rate estimation.

March 12, 2008Fan's MS Defense35 Publications related to this work F. Wang and V. D. Agrawal, “Single Event Upset: An Embedded Tutorial,” in Proc. 21st IEEE International Conference on VLSI Design, January 2008, pp F. Wang and V. D. Agrawal, “Soft Error Rate Determination for Nanometer CMOS VLSI Circuits,” in Proc. 40th IEEE Southeastern Symposium on System Theory, March 16-18, 2008, Paper TA1. F. Wang and V. D. Agrawal, “Probabilistic Soft Error Rate Estimation from Statistical SEU Parameters,” in Proc. 17 th IEEE North Atlantic Test Workshop, May Unpublished work: F. Wang and V. D. Agrawal, “Soft Error Considerations for Computer Web Servers”.

March 12, 2008Fan's MS Defense36 References [1] R. R. Rao, K. Chopra, D. Blaauw, and D. Sylvester, “An Efficient Static Algorithm for Computing the Soft Error Rates of Combinational Circuits," Proceedings of the conference on Design automation and test in Europe: Proceedings, pp , [2] R. Rajaraman, J. S. Kim, N. Vijaykrishnan, Y. Xie, and M. J. Irwin, “SEAT-LA: A Soft Error Analysis Tool for Combinational Logic," VLSI Design, th International Conference on, 2006, pp [3] G. Asadi and M. B. Tahoori, “An Accurate SER Estimation Method Based on Propagation Probability,” Proc. Design Automation and Test in Europe Conf,2005, pp [4] M. Zhang and N. R. Shanbhag, “A soft error rate analysis (SERA) methodology," in IEEE/ACM International Conference on Computer Aided Design, ICCAD-2004, 2004, pp [5] T. Rejimon and S. Bhanja, “An Accurate Probabilistic Model for Error Detection," in 18th International Conference on VLSI Design, 2005, pp [6] J. Graham, “Soft errors a problem as SRAM geometries shrink,“ ebn, 28 Jan [7] Wingyu Leung; Fu-Chieh Hsu; Jones, M. E., "The ideal SoC memory: 1T-SRAM TM," Proc.13th Annual IEEE International on ASIC/SOC Conference, vol., no., pp.32-36, 2000 [8] Report, “Soft Errors in Electronic Memory-A White Paper," Technical report, Tezzaron Semiconductor, 2004.

March 12, 2008Fan's MS Defense37 Thank You...