Statistical Full-Chip Leakage Analysis Considering Junction Tunneling Leakage Tao Li Zhiping Yu Institute of Microelectronics Tsinghua University.

Slides:



Advertisements
Similar presentations
Variation Aware Gate Delay Models Dinesh Ganesan.
Advertisements

Probabilistic Inverse Dynamics for Nonlinear Blood Pattern Reconstruction Benjamin Cecchetto, Wolfgang Heidrich University of British Columbia.
Non-Gaussian Statistical Timing Analysis Using Second Order Polynomial Fitting Lerong Cheng 1, Jinjun Xiong 2, and Lei He 1 1 EE Department, UCLA *2 IBM.
Design Rule Generation for Interconnect Matching Andrew B. Kahng and Rasit Onur Topaloglu {abk | rtopalog University of California, San Diego.
Algorithm for Fast Statistical Timing Analysis Jakob Salzmann, Frank Sill, Dirk Timmermann SOC 2007, Nov ‘07, Tampere, Finland Institute of Applied Microelectronics.
Tunable Sensors for Process-Aware Voltage Scaling
Uncertainty Quantification & the PSUADE Software
On the Need for Statistical Timing Analysis Farid N. Najm University of Toronto
Reap What You Sow: Spare Cells for Post-Silicon Metal Fix Kai-hui Chang, Igor L. Markov and Valeria Bertacco ISPD’08, Pages
Leakage and Dynamic Glitch Power Minimization Using MIP for V th Assignment and Path Balancing Yuanlin Lu and Vishwani D. Agrawal Auburn University ECE.
Stochastic Analog Circuit Behavior Modeling by Point Estimation Method
0 1 Width-dependent Statistical Leakage Modeling for Random Dopant Induced Threshold Voltage Shift Jie Gu, Sachin Sapatnekar, Chris Kim Department of Electrical.
Parameterized Timing Analysis with General Delay Models and Arbitrary Variation Sources Khaled R. Heloue and Farid N. Najm University of Toronto {khaled,
1 Closed-Loop Modeling of Power and Temperature Profiles of FPGAs Kanupriya Gulati Sunil P. Khatri Peng Li Department of ECE, Texas A&M University, College.
Yuanlin Lu Intel Corporation, Folsom, CA Vishwani D. Agrawal
Non-Linear Statistical Static Timing Analysis for Non-Gaussian Variation Sources Lerong Cheng 1, Jinjun Xiong 2, and Prof. Lei He 1 1 EE Department, UCLA.
Penn ESE535 Spring DeHon 1 ESE535: Electronic Design Automation Day 23: April 22, 2009 Statistical Static Timing Analysis.
Bayesian Analysis of X-ray Luminosity Functions A. Ptak (JHU) Abstract Often only a relatively small number of sources of a given class are detected in.
UPM, Faculty of Computer Science & IT, A robust automated attendance system using face recognition techniques PhD proposal; May 2009 Gawed Nagi.
1 Dynamic Power Estimation With Process Variation Modeled as Min–Max Delay Jins Davis Alexander Vishwani D. Agrawal Department of Electrical and Computer.
Penn ESE535 Spring DeHon 1 ESE535: Electronic Design Automation Day 14: March 19, 2008 Statistical Static Timing Analysis.
1 A Single-supply True Voltage Level Shifter Rajesh Garg Gagandeep Mallarapu Sunil P. Khatri Department of Electrical and Computer Engineering, Texas A&M.
NTHU-CS VLSI/CAD LAB TH EDA Student : Da-Cheng Juan Advisor : Shih-Chieh Chang Fine-Grained Sleep Transistor Sizing Algorithm for Leakage Power Minimization.
Independent Component Analysis (ICA) and Factor Analysis (FA)
Jan. 2007VLSI Design '071 Statistical Leakage and Timing Optimization for Submicron Process Variation Yuanlin Lu and Vishwani D. Agrawal ECE Dept. Auburn.
SAMSON: A Generalized Second-order Arnoldi Method for Reducing Multiple Source Linear Network with Susceptance Yiyu Shi, Hao Yu and Lei He EE Department,
Lecture 5 – Power Prof. Luke Theogarajan
Statistical Gate Delay Calculation with Crosstalk Alignment Consideration Andrew B. Kahng, Bao Liu, Xu Xu UC San Diego
Lecture 7: Power.
Laurent Itti: CS599 – Computational Architectures in Biological Vision, USC Lecture 7: Coding and Representation 1 Computational Architectures in.
Trace-Based Framework for Concurrent Development of Process and FPGA Architecture Considering Process Variation and Reliability 1 Lerong Cheng, 1 Yan Lin,
EE 241 Class Project Substrate Noise Current Injected by Digital IP Cores Stefano Zanella Mentor: Luca Carloni.
Lecture 21, Slide 1EECS40, Fall 2004Prof. White Lecture #21 OUTLINE –Sequential logic circuits –Fan-out –Propagation delay –CMOS power consumption Reading:
Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.
Advanced Computing and Information Systems laboratory Device Variability Impact on Logic Gate Failure Rates Erin Taylor and José Fortes Department of Electrical.
EE466: VLSI Design Power Dissipation. Outline Motivation to estimate power dissipation Sources of power dissipation Dynamic power dissipation Static power.
MOS Capacitors MOS capacitors are the basic building blocks of CMOS transistors MOS capacitors distill the basic physics of MOS transistors MOS capacitors.
1 Delay Estimation Most digital designs have multiple data paths some of which are not critical. The critical path is defined as the path the offers the.
Short Resume of Statistical Terms Fall 2013 By Yaohang Li, Ph.D.
Introduction to variable selection I Qi Yu. 2 Problems due to poor variable selection: Input dimension is too large; the curse of dimensionality problem.
Probabilistic Mechanism Analysis. Outline Uncertainty in mechanisms Why consider uncertainty Basics of uncertainty Probabilistic mechanism analysis Examples.
A Class Presentation for VLSI Course by : Fatemeh Refan Based on the work Leakage Power Analysis and Comparison of Deep Submicron Logic Gates Geoff Merrett.
Ashley Brinker Karen Joseph Mehdi Kabir ECE 6332 – VLSI Fall 2010.
Statistical Sampling-Based Parametric Analysis of Power Grids Dr. Peng Li Presented by Xueqian Zhao EE5970 Seminar.
Bayesian Macromodeling for Circuit Level QCA Design Saket Srivastava and Sanjukta Bhanja Department of Electrical Engineering University of South Florida,
Direct Message Passing for Hybrid Bayesian Networks Wei Sun, PhD Assistant Research Professor SFL, C4I Center, SEOR Dept. George Mason University, 2009.
Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.
ECE 8443 – Pattern Recognition LECTURE 10: HETEROSCEDASTIC LINEAR DISCRIMINANT ANALYSIS AND INDEPENDENT COMPONENT ANALYSIS Objectives: Generalization of.
Center for Radiative Shock Hydrodynamics Fall 2011 Review Assessment of predictive capability Derek Bingham 1.
ECE 8443 – Pattern Recognition ECE 8423 – Adaptive Signal Processing Objectives: ML and Simple Regression Bias of the ML Estimate Variance of the ML Estimate.
CSC2515: Lecture 7 (post) Independent Components Analysis, and Autoencoders Geoffrey Hinton.
Expectation-Maximization (EM) Algorithm & Monte Carlo Sampling for Inference and Approximation.
PCA vs ICA vs LDA. How to represent images? Why representation methods are needed?? –Curse of dimensionality – width x height x channels –Noise reduction.
ECE 8443 – Pattern Recognition ECE 8527 – Introduction to Machine Learning and Pattern Recognition LECTURE 12: Advanced Discriminant Analysis Objectives:
A Class presentation for VLSI course by : Maryam Homayouni
Feature Selection and Extraction Michael J. Watts
Z. Feng MTU EE4800 CMOS Digital IC Design & Analysis 6.1 EE4800 CMOS Digital IC Design & Analysis Lecture 6 Power Zhuo Feng.
Joshua L. Garrett Digital Circuits Design GroupUniversity of California, Berkeley Compact DSM MOS Modeling for Energy/Delay Estimation Joshua Garrett,
Fault-Tolerant Resynthesis for Dual-Output LUTs Roy Lee 1, Yu Hu 1, Rupak Majumdar 2, Lei He 1 and Minming Li 3 1 Electrical Engineering Dept., UCLA 2.
LECTURE 11: Advanced Discriminant Analysis
Defining Statistical Sensitivity for Timing Optimization of Logic Circuits with Large-Scale Process and Environmental Variations Xin Li, Jiayong Le, Mustafa.
Principal Component Analysis (PCA)
Samuel Luckenbill1, Ju-Yueh Lee2, Yu Hu3, Rupak Majumdar1, and Lei He2
EE201C Modeling of VLSI Circuits and Systems Final Project
Challenges in Nanoelectronics: Process Variability
Survey on Stochastic Leakage Modeling Wenyao Xu Fengbo Ren
Impact of Parameter Variations on Multi-core chips
Post-Silicon Calibration for Large-Volume Products
Data Analysis – Part1: The Initial Questions of the AFCS
Parametric Yield Estimation Considering Leakage Variability Rajeev Rao, Anirudh Devgan, David Blaauw, Dennis Sylvester Present by Fengbo Ren Apr. 30.
Presentation transcript:

Statistical Full-Chip Leakage Analysis Considering Junction Tunneling Leakage Tao Li Zhiping Yu Institute of Microelectronics Tsinghua University

2 Outline Motivation Motivation Junction Tunneling Leakage – Circuit Level Analysis Junction Tunneling Leakage – Circuit Level Analysis –Simple inverter –Multi-input gate Statistical Full-Chip Leakage Analysis Technique Statistical Full-Chip Leakage Analysis Technique –Modeling of process-induced parameter variations PCA & ICA PCA & ICA –Sum of leakage components Experimental Results Experimental Results Summary Summary

3 Leakage and Process Variations Leakage power becomes a major component of the total power. Leakage power becomes a major component of the total power. Process variation has a significant impact on leakage. Process variation has a significant impact on leakage  0.18  0.13  0.1  Leakage Power Active Power Power (W) Feature Size Scale Down 0.18 μm0.09 μm 65 nm

4 Major Leakage components Subthreshold leakage Subthreshold leakage Gate oxide leakage Gate oxide leakage Junction tunneling leakage Junction tunneling leakage Gate Source n+ Bulk Drain Subthreshold Leakage I sub Gate Leakage I gate Junction tunneling leakage

5 Overview of Related Works Previous works on statistical full-chip leakage computation Previous works on statistical full-chip leakage computation –Computation of PDF of full-chip leakage Approximate process variations as Gaussian distributions Approximate process variations as Gaussian distributions Finding full-chip leakage by summing up independent lognormals Finding full-chip leakage by summing up independent lognormals R. Rao ISLPED03,H. Chang ICCAD 03, H. Chang DAC05, X. Li DAC06, et al. R. Rao ISLPED03,H. Chang ICCAD 03, H. Chang DAC05, X. Li DAC06, et al. Most of the previous works ignored Most of the previous works ignored –Effect of Non-Gaussian parameters –Junction tunneling leakage

6 Outline Motivation Motivation Junction tunneling leakage – Circuit level analysis Junction tunneling leakage – Circuit level analysis –Simple inverter –Multi input gate Statistical Full-chip leakage analysis technique Statistical Full-chip leakage analysis technique –Modeling of process-induced parameter variations PCA & ICA PCA & ICA –Sum of leakage components Experimental Results Experimental Results Summary Summary

7 Simple Inverter When input = 0V When input = 0V –NMOS: maximum & Can be independently calculated and added for total leakage Can be independently calculated and added for total leakage –PMOS: gate oxide leakage – small and ignored When input = When input = –NMOS: gate oxide leakage –PMOS: subthreshold leakage and junction tunneling leakage 0 0

8 Multi input gate: general approach If all inputs have a high state If all inputs have a high state –Analysis is similar to the that of the inverter At least one input is low At least one input is low –Combination of,,and –Approach: distinguish 6 different scenarios

9 Total leakage current of a chip: Total leakage current of a chip: Computation of Total Chip Leakage Input pattern independent approach Input pattern independent approach –Direct computation: 2 k input vector states for a k-input gate –Applying dominant states of Leakage of stack at state i is not always independent Leakage of stack at state i is not always independent –Interactions of I sub, I gate and I junc need to be considered –Analyzing leakage current of stack by input state : probability of input vector state i of the j th gate can be either the leakage for a fixed input vector or the average leakage current

10 Dominant States of Leakage Current Case (a) (c): dominate states of I gate Case (a) (c): dominate states of I gate NMOS-Transistor Stack Interaction between I sub and I gate Interaction between I sub and I gate D. Lee et. al. at DAC03 C. Oh et. al. at DAC99 D. Lee et. al. at DAC03 Dominant states of junction tunneling leakage I junc Dominant states of junction tunneling leakage I junc –States with the “on” transistors connected to the output node (stack effect ) –Only k dominant states for a k-input gate Case (a) (b): dominate states of I sub Case (a) (b): dominate states of I sub

11 Results: Leakage estimation for 4-NAND The error of the proposed analysis method over SPICE The error of the proposed analysis method over SPICE Average ~1.5% over all input states Average ~1.5% over all input states Maximum error = Maximum error =

12 Outline Motivation Motivation Junction tunneling leakage – Circuit level analysis Junction tunneling leakage – Circuit level analysis –Simple inverter –Multi input gate Statistical Full-chip leakage analysis technique Statistical Full-chip leakage analysis technique –Modeling of process-induced parameter variations PCA & ICA PCA & ICA –Sum of leakage components Experimental Results Experimental Results Summary Summary

13 Proposed Analysis Method Highlights Incorporates both Gaussian and non-Gaussian parameters Non-Gaussian and Gaussian variables transformed to independent basis with PCA/ICA Uses closed form PDF/CDF expressions Moments matching-based PDF/CDF extraction Fast algorithm for the sum up of leakage components Three kinds of leakage components are considered Inputs are moments of varying process parameters Easier to obtain moments from process data files

14 Outline Motivation Motivation Junction tunneling leakage – Circuit level analysis Junction tunneling leakage – Circuit level analysis –Simple inverter –Multi input gate Statistical Full-chip leakage analysis technique Statistical Full-chip leakage analysis technique –Modeling of process-induced parameter variations PCA & ICA PCA & ICA –Sum of leakage components Experimental Results Experimental Results Summary Summary

15 Experimental Results Comparison of our results with Monte Carlo simulations Comparison of our results with Monte Carlo simulations Comparison with Gaussian modeling of parameters Comparison with Gaussian modeling of parameters BenchmarkOur Method Error ((Our-MC)/MC)%Gaussian Error ((Old-MC)/MC)% Name#Cells#Grids µ σ95% Pt5% Pt µ σ95% Pt5% Pt C C C C C C C C

16 Outline Motivation Motivation Junction tunneling leakage – Circuit level analysis Junction tunneling leakage – Circuit level analysis –Simple inverter –Multi input gate Statistical Full-chip leakage analysis technique Statistical Full-chip leakage analysis technique –Modeling of process-induced parameter variations PCA & ICA PCA & ICA –Sum of leakage components Experimental Results Experimental Results Summary Summary

17 Summary A fast approach to compute total leakage current A fast approach to compute total leakage current –Considering,,and –Average error 1.5% Both Gaussian and Non-Gaussian parameters are considered Both Gaussian and Non-Gaussian parameters are considered –PCA and ICA are employed as preprocessing steps Sum the leakage to get a final result Sum the leakage to get a final result Algorithm has a complexity of Algorithm has a complexity of

18 Thanks!