Spectral_BIST - Spectral BIST Alok Doshi Anand Mudlapur 1...

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1 Spectral BIST Alok Doshi Anand Mudlapur
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2 Overview Introduction to spectral testing Previous work Application of RADEMACHER – WALSH spectrum in testing and design of digital circuits Spectral techniques for sequential ATPG Spectral methods for BIST in SOC Spectral Analysis for statistical response compaction during BIST Modifying spectral TPG using selfish gene algorithm Proposed improvements Results
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3 Introduction Projection of time varying vectors in the frequency domain. PI1 : 11001100 PI2 : 11110000 PI3 : 00111100 PI4 : 01010101
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4 Basic idea Meaningful inputs (e.g., test vectors) of a circuit are not random. Input signals must have spectral characteristics that are different from white noise (random vectors). Sequential circuit tests are not random: Some PIs are correlated. Some PIs are periodic. Correlation and periodicity can be represented by spectral components, e.g., Hadamard coefficients.
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5 Statistics of Test Vectors 100% coverage test Test vectors are not random: Correlation: a = b , frequently. Weighting: c has more 1s than a or b . a 00011 b 01100 c 10101 a b c
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6 Primary Motivation We want to extract the information embedded in the input signals and output responses Hence, we apply signal processing techniques to extract this information In order to meet the above objective, we make use of frequency decomposition techniques. i.e. A signal can be projected to a set of independent, periodic waveforms that have different frequencies. This set of waveforms, forms the basis matrix
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7 Primary Motivation (cont.) The projection operation reveals the quantity that each basis vector contributes to the original signal This quantity is called decomposition coefficient With the aid the decomposed information, one can easily enhance the important frequencies and suppress the unimportant ones This process leads us to a new and better quality signal, easing the complexity (in our case i.e. of test generation)
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8 Hadamard Transform The projection matrix choosen is the Hadamard transform as it is a well-known non-sinusoidal orthogonal transform used in signal processing. A Hadamard matrix consists of only 1’s and -1’s, which makes it a good choice for the signals in VLSI testing (1 = logic 1, -1 = logic 0). Each basis (row/column) in the Hadamard matrix is a distinct sequence that characterizes the switching frequency between 1s and -1s.
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9 Hadamard Transform (cont.) Hadamard matrices are square matrices containing only 1s and –1s. They can be generated recursively using the formula, where, H(0) = 1 and n = log 2 N
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10 Hadamard Transform (cont.)
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11 Hadamard transforms over FFT The Walsh functions can be interpreted as binary (sampled) versions of the sin and cos, which are the basic functions of the Discrete Fourier Transform. This interpretation led to the name BInary
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This note was uploaded on 09/17/2011 for the course ELEC 6970 taught by Professor Staff during the Spring '08 term at Auburn University.

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Spectral_BIST - Spectral BIST Alok Doshi Anand Mudlapur 1...

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