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Achieving automated shmoo results analysis with a


        deep learning method


        By Chao Zhou  [Teradyne Inc.]
        T        he shmoo plot is a widely-  Proposed model introduction        To make the tool compatible with




                 u s e d t e c h n i q u e  i n t h e
                                             The structure  of this  model is  a
                 semiconductor industry for   modified AlexNet, proposed by Alex   shmoos  of  different  sizes,  a  3-level
                                                                              (4, 2, 1) SPP layer, which is inserted
        evaluating product specifications and   Krizhevsky [3], which demonstrated   between the convolutional layers and
        debugging  test  vectors.  It  examines   that the self-learning features of neural   the FC layers, is introduced in this CNN
        the characterization of supply voltage   networks can surpass manual capabilities.   model (Figure 3). The SPP layer is used
        and  operating  frequency,  providing   The neural network structure of this   to  convert  different  shapes  of  input
        valuable  information  for  improving   method is shown in Figure 2. A total   into a fixed value, which is required as
        yield  ratios.  However,  the  manual   of eight layers of convolutional neural   the input size of the subsequent fully-
        review process of shmoo plot results,   networks (CNNs) are used, including four   connected layer. The core principle of
        particularly during test with automated   layers of convolution (feature extraction),   the SPP layer is to use multiple level
        t e s t  e q u ip m e nt  (AT E),  i s  t i m e -  one spatial pyramid pooling (SPP) layer [4]   pooling of different sizes (4*4, 2*2 and
        consuming and can prolong the test   to process the variable size of the input,   1*1 pooling windows are used in this
        period, delaying time to market. To   two layers of fully-connected (FC) layers   method) to process the output of the last
        address this challenge, a deep learning   (classification) and one output layer.  convolutional layer. Then, it combines
        based method has been developed and   The first four convolutional layers are to   the results to obtain three (16*ch, 4*ch
        implemented to analyze shmoo results   extract the features of the shmoo plot. The   and  1*ch)  level  feature  vectors  [4].
        automatically. This method, developed   convolution kernel shape of all layers in   Finally, after flattening these feature
        using PyTorch, can accurately analyze   the network is 3x3, while the shape of the   vectors, they are passed to the next fully-
        sh moo resu lt s i n a shor ter t i me   shmoo plots in the training data set are all   connected layer.
        compared to manual methods, and    11x11, which means that a large convolution   The role of the fully-connected
        can be seamlessly integrated into any   kernel to extract the pattern is not   layer is to do the classification. The
        existing test environment.         necessary. A small 3x3 window to capture   SPP layer is connected to an FC layer
          In the field of ATE, there are several   a single pixel and its surrounding pixels   with 84-channels, at which point the
        techniques described in the literature to   is sufficient. Padding is used to preserve   extracted features output by the SPP
        analyze shmoo data, such as decision   the pixel information of the shmoo edge to   layer pass through two fully-connected
        tree, logistic regression, support vector   avoid misjudging missing edge pixels. The   layers. A one-dimensional array with
        machines, and random forest  [1-2].   padding size is set to 1, which, coordinated   six elements is output as the result, with
        These methods are traditional machine   with the convolution kernel, can make the   these six elements representing the Fail,
        learning techniques, which require the   height and width of each convolutional   Pass,  Vol,  Freq,  Marginal  and  Hole
        results to be manually reviewed, thereby   layer’s output equal to 11x11.  values, respectively.
        limiting their application and resulting in
        poor test accuracy in some cases.
          Deep learning, deployed in this
        new application, is a machine learning
        method in which a neural network with
        hidden layers is used to simulate the
        operation of a human’s neural actions,
        extracting the features of the input signal
        and executing the classification work
        without human intervention. Nowadays,
        deep learning applications in computer
        vision and natural language processing
        far exceed those of traditional machine
        learning methods. In this application, a
        neural network was adapted to analyze
        and interpret the characteristics of a
        shmoo plot, significantly improving test
        accuracy (Figure 1).
                                           Figure 1: Functional diagram of a deep learning method.

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