Jin-soo Jung

Tolerance Improvement for Organic Embedded Passive RF Module Substrate

In this paper, new embedded passive technology process and design for higher tolerance RF module are introduced. The embedded passive technology in organic substrate [1] is a new technical trend that offers promising packaging advantages over other available technology like LTCC however, there are still some technical challenges that need to be overcome. The tolerance is considered to be an important issue in organic embedded passive technology, especially, for high frequency mobile applications [2]. The application described in this paper is an RF filter designed for front-end modules intended for wireless communication such as IEEE802.il (WiFi), IEEE802.16 (WiMAX) and Bluetooth applications [2]. The operating frequency of these standards range from more than 1GHz up to over 5 GHz therefore the tolerance is considered as one of the important design issue in the development phase. Embedded passive in Organic substrate has been described to have advantage over classic SMT(surface mount technology) and LTCC (low temperature co-fired ceramic) because it offers smaller form factor with high level of passive integration thus achieving lower cost in RF module applications for next generation. However at this moment, one known technical challenge in organic substrate is that its tolerance is not still good enough when used at module applications which require stringent tolerance in RF. This paper focuses on a new design and process method which could provide an acceptable level of improvement in each embedded passive components tolerance by studying mainly the factors coming from metal layers line variations. The new process and design is suitable to improve metal layers etching variation, with current available embedded capacitor material, for embedded capacitor and embedded inductor to describe embedded filters.

Generalization of the codebook updating method of the k-means algorithm

At each iteration of codebook training, the K-means algorithm finds a new codevector and partition by satisfying the centroid condition and the nearest neighbor condition alternatively and satisfies the iterative descent property. We generalize the codebook updating method of the K-means algorithm without affecting the iterative descent property. We also present and re-interpret several algorithms that are included into the category of the generalized K-means algorithm.