研究生: |
郭峻樺 Kuo, Jun-Hua |
---|---|
論文名稱: |
利用多重時脈測試資料降低測試成本達成修復效能良率 Test Cost Reduction for Performance Yield Recovery by Classification of Multiple-Clock Test Data |
指導教授: |
劉靖家
Liou, Jing-Jia |
口試委員: |
陳竹一
溫宏斌 |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
論文出版年: | 2012 |
畢業學年度: | 100 |
語文別: | 英文 |
論文頁數: | 55 |
中文關鍵詞: | 製程變異 、可調變電壓電路 、測試成本 、機器學習 |
相關次數: | 點閱:2 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著超大型積體電路製程技術愈來愈進步,當電晶體製程尺寸進入奈米等級之後,製程變異(process variations)的影響已經變成是造成晶片良率下降的主要原因之一,近年來,補償製程變異的後矽電路調整技術已經廣泛的被研究,例如用全電路電壓調整(full-chip tuning),但該技術將會在沒有受到製程變異的電路部分增加額外的動態功率或靜態功率消耗。所以我們之前提出以列電壓為基礎的調控電路結構(row-based tunable architecture),此結構是可逐列調整以列為單位的電路操作電壓(supply voltage),藉由測試診斷技術和以滿足性為基礎(SAT-based)的調整電壓設定演算法來做增加電路的操作速度或降低電路的功率消耗來做最佳化的電路電壓調整。不過上述方法相關的測試診斷技術需要耗費相當大量的測試成本,而測試成本來自於多重測試時脈方法量測電路所需要的延遲路徑。
在本篇論文中,我們提出的方法將使用固定少量的測試時脈建立測試資料,而我們就可以使用事先建立的測試資料來分類晶片測試結果且把該晶片分類到不同的電壓設定,這樣就可節省多重測試時脈測試的測試成本。在實驗結果顯示我們所提出來的方法,相較於藉由測試診斷技術和以滿足性為基礎的調整電壓設定演算法的方法平均約只要花費4%的測試成本,另外調整過電路操作電壓設定的晶片樣本可修復良率達到86%到118%。
[1] M. Choi and L. Milor, “Impact on circuit performance of deterministic within-die variation
in nanoscale semiconductor manufacturing,” IEEE Transactions on Computer-Aided Design
of Integrated Circuits and Systems, 2006.
[2] K. Bowman, S. Duvall, and J. Meindl, “Impact of die-to-die and within-die parameter fluctu-
ations on the maximum clock frequency distribution for gigascale integration,” IEEE Journal
of Solid-State Circuits, 2002.
[3] S. Borkar, T. Karnik, S. Narendra, J. Tschanz, A. Keshavarzi, and V. De, “Parameter varia-
tions and impact on circuits and microarchitecture,” ACM/IEEE Design Automation Confer-
ence, 2003.
[4] H. Aikawa, T. Sanuki, A. Sakata, E. Morifuji, H. Yoshimura, T. Asami, H. Otani, and H. Oya-
matsu, “Compact Model for Layout Dependent Variability,” in International Electron Devices
Meeting, 2009.
[5] Y. Ye, F. Liu, M. Chen, and Y. Cao, “Variability analysis under layout pattern-dependent
rapid-thermal annealing process,” in Proceedings of Design Automation Conference, 2009.
[6] J. Tschanz, J. Kao, S. Narendra, R. Nair, D. Antoniadis, A. Chandrakasan, and V. De, “Adap-
tive body bias for reducing impacts of die-to-die and within-die parameter variations on mi-
croprocessor frequency and leakage,” IEEE Journal of Solid-State Circuits, 2002.
[7] J. Gregg and T. Chen, “Post Silicon Power/Performance Optimization in the Presence of
Process Variations Using Individual Well-Adaptive Body Biasing,” IEEE Transactions on
Very Large Scale Integration (VLSI) Systems, 2007.
[8] J. Gregg and T. chen, “Optimization of individual well adaptive body biasing (IWABB) using
a multiple objective evolutionary algorithm,” International Symposium on Quality of Elec-
tronic Design, 2005.
[9] T. Chen and J. Gregg, “A low cost individual-well adaptive body bias (IWABB) scheme
for leakage power reduction and performance enhancement in the presence of intra-die varia-
tions,” Proceedings of the Design, Automation and Test in Europe Conference and Exhibition,
2004.
[10] M. Elgebaly and M. Sachdev, “Variation-Aware Adaptive Voltage Scaling System,” IEEE
Transactions on Very Large Scale Integration (VLSI) Systems, 2007.
[11] Y. Oowaki, M. Noguchi, S. Takagi, D. Takashima, M. Ono, Y. Matsunaga, K. Sunouchi,
H. Kawaguchiya, S. Matsuoka, M. Kamoshida, T. Fuse, S. Watanabe, A. Toriumi, S. Manabe,
and A. Hojo, “A sub-0.1 um circuit design with substrate-over-biasing,” Digest of Technical
Papers. IEEE International Solid-State Circuits Conference, 1998.
[12] S. Narendra, A. Keshavarzi, B. Bloechel, S. Borkar, and V. De, “Forward body bias for
microprocessors in 130-nm technology generation and beyond,” IEEE Journal of Solid-State
Circuits, 2003.
[13] V. Khandelwal and A. Srivastava, “Active Mode Leakage Reduction Using Fine-Grained
Forward Body Biasing Strategy,” Proceedings of the International Symposium on Low Power
Electronics and Design, 2004.
[14] N. Jayakumar, S. Dhar, and S. Khatri, “A self-adjusting scheme to determine the optimum
RBB by monitoring leakage currents,” ACM/IEEE Design Automation Conference, 2005.
[15] J. Tschanz, S. Narendra, R. Nair, and V. De, “Effectiveness of adaptive supply voltage and
body bias for reducing impact of parameter variations in low power and high performance
microprocessors,” IEEE Journal of Solid-State Circuits, 2003.
[16] Y.-Y. Chen and J.-J. Liou, “Diagnosis framework for locating failed segments of path delay
faults,” IEEE Transactions on VLSI Systems, 2008.
[17] A. Krstic, L. C. Wang, J. J. Liou, and M. S. Abadir, “Diagnosis-based post-silicon timing val-
idation using statistical timing tools and methodologies,” Proceedings of IEEE International
Test Conference, 2003.
[18] K. Yang and K.-T. Cheng, “Timing-reasoning-based delay fault diagnosis,” Proceedings of
Design, Automation and Test in Europe, 2006.
[19] J.-J. Liou, Y.-Y. Chen, C.-C. Chen, C.-Y. Chien, and K.-L. Wu, “Diagnosis-assisted Supply
Voltage Configuration to Increase Performance Yield of Cell-Based Designs,” Proceedings
of Asia & South Pacific Design Automation Conference, 2011.
[20] Y.-Y. Chen and J.-J. Liou, “A non-intrusive and accurate inspection method for segment delay
variabilities,” Proceedings of IEEE Asian Test Symposium, 2009.
[21] C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001, software
available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.
[22] M. Chun-Chia Chen, “Design Methodology and Simulation Framework for Variation-Aware
Cell-based Tunable Circuits,” 2008.
[23] R. Duda, P. Hart, and D. Stork, Pattern classification. Wiley, 2001.
[24] A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: A review,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2000.
[25] V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
[26] C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification,”
Department of Computer Science, National Taiwan University, Tech. Rep., 2003.
[27] C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,”
IEEE Transactions on Neural Networks, 2002.
[28] C. E. Shannon, W. Weaver, and Shannon, The Mathematical Theory of Communcation. Uni-
versity of Illinois Press, 1998.
[29] R. Battiti, “Using mutual information for selecting features in supervised neural net learning,”
IEEE Transactions on Neural Networks, 1994.
[30] H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria
of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, 2005.
[31] P. A. Est´evez, M. Tesmer, C. A. Perez, and J. M. Zurada, “Normalized mutual information
feature selection,” IEEE Transactions on Neural Networks, 2009.
[32] B. Brumen, T. Welzer, I. Rozman, M. Holbl, and H. Jaakkola, “Convergence detection criteria
for classification based on final error rate,” IEEE International Conference on Computational
Cybernetics, 2005.
[33] B. Brumen, M. B. Juriˇc, T. Welzer, I. Rozman, H. Jaakkola, and A. Papadopoulos, “Assess-
ment of classification models with small amounts of data,” Informatica, 2007.
[34] B. Brumen, I. Golob, H. Jaakkola, T. Welzer, and I. Rozman, “Early assessment of classi-
fication performance,” in Proceedings of the second workshop on Australasian information
security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32,
2004.
[35] W. Lehnert, J. Mccarthy, S. Soderland, E. Riloff, C. Cardie, J. Peterson, F. Feng, C. Dolan,
and S. Goldman, “Umass/hughes: Description Of The Circus System Used For Muc-5,” 1993.
[36] A. Agarwal, D. Blaauw, V. Zolotov, S. Sundareswaran, M. Zhao, K. Gala, and R. Panda,
“Statistical delay computation considering spatial correlations,” Proceedings of Asia & South
Pacific Design Automation Conference, 2003.
[37] L.-T. Wang, C. E. Stroud, and N. A. Touba, System-on-Chip Test Architectures: Nanometer
Design for Testability. Morgan Kaufmann, 2007.
[38] J. jia Liou, A. Krstic, L.-C. Wang, and K. ting Cheng, “False-path-aware statistical timing
analysis and efficient path selection for delay testing and timing validation,” Proceedings of
Design Automation Conference, 2002.
[39] I.-S. Oh, J.-S. Lee, and B.-R. Moon, “Hybrid genetic algorithms for feature selection,” IEEE
Trans. Pattern Anal. Mach. Intell., 2004.
[40] J. Huang, Y. Cai, and X. Xu, “A wrapper for feature selection based on mutual information,”
Pattern Recognition, International Conference on, 2006.