Jun Fan

Bio: Jun Fan is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Equivalent circuit & Printed circuit board. The author has an hindex of 36, co-authored 482 publications receiving 5641 citations. Previous affiliations of Jun Fan include Ulsan National Institute of Science and Technology & University of Missouri.

Fast method for EIS sweep test

Abstract: This paper proposed a novel fast band-sweep measurement method for efficient isotropic sensitivity (EIS) of wireless devices. The method is built on the facts that the relationship between the bit error rate (BER) and the received power is generally consistent in the same band, and the EIS values at a fixed angle is slowly-changing on the frequency except harmonics. Therefore, only few frequency points are required to be measured for a whole band sweep test resulting in a significantly decrease in measurement time.

PEEC-Based On-chip PDN Impedance Modeling Using Layered Green's Function

Abstract: This paper presents an impedance model of on-chip power distribution network (PDN), which is an efficient criterion for estimating simultaneous switching noises (SSNs) on 3-D integrated circuit (IC). The impedance of on-chip PDN, including the effect of silicon substrate, is accurately modeled based on partial element equivalent circuit (PEEC) and layered Green's function (LGF). The equivalent circuit model of PDN is extracted based on the physical dimensions and electrical material characteristic of PDN at first. And then the LGF is used to consider the effect of silicon substrate for improving the accuracy of on-chip PDN impedance model. The effectiveness of proposed model has been validated by full wave simulation. The high order resonance of PDN impedance can also be accurately predicted.

Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction

Abstract: Simulating the dynamic characteristics of a PN junction at the microscopic level requires solving the Poisson's equation at every time step. Solving at every time step is a necessary but time-consuming process when using the traditional finite difference (FDM) approach. Deep learning is a powerful technique to fit complex functions. In this work, deep learning is utilized to accelerate solving Poisson's equation in a PN junction. The role of the boundary condition is emphasized in the loss function to ensure a better fitting. The resulting I-V curve for the PN junction, using the deep learning solver presented in this work, shows a perfect match to the I-V curve obtained using the finite difference method, with the advantage of being 10 times faster at every time step.

2X-Thru De-embedding for Non-2 N Even Number Port Network

Abstract: 2N–port de-embedding has been well studied previously by using the higher order modal-based S-parameters. Such idea is successfully validated by using the 2X-Thru de-embedding, as well as the classic TRL. Non-2N even number (such as 6, 10, 12, etc.) port network S-parameters de-embedding is derived and validated in this paper. By inserting a factitious single-ended 2X-Thru before the fixture characterization, the derivation and validation are demonstrated through a 6-port 2X-Thru de-embedding example. After the fixture characterization, the inserted artificially single-ended 2X-Thru will be removed before the step of fixture removing calculation. As the 2X-Thru de-embedding application always has even number of port 2X-Thru fixtures, the idea in this paper extend the 2X-Thru de-embedding to any arbitrary number of port. The derivation and justification are also suitable for other de-embedding algorithms with even number of port.

Reduction of Mode Conversion of Differential-Mode Noise to Common-Mode Noise by Printed Circuit Board Modification for Unbalanced EMI Filter Network

Abstract: This paper presents a method for the reduction of mode conversion from differential-mode (DM) noise to common-mode (CM) noise in an unbalanced EMI filter. The unbalanced nature in the EMI filter is a result of not incorporating all the required filter components due to space and cost constraints or due to the parasitic impedances of a printed circuit board (PCB). It is demonstrated that the reduction of mode conversion from DM noise to CM noise can be achieved by modifying the current path on the ground (GND) layer of the PCB of the unbalanced EMI filter. The currents on the GND layer are guided to take a longer route by introducing a "cutout" to the ground plane in the PCB, which increases the impedance of the current path on the GND layer. This approach does not require additional components to the EMI filter for the reduction of CM noise due to the mode conversion. Simulations show that the cutout decreases the CM noise converted from the DM noise by at least 4 dB in the AM radio frequency band (530 kHz – 1.8 MHz).

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

IEEE Transactions on Microwave Theory and Techniques and Antennas and Propagation Announce a Joint Special Issue on Ultra-Wideband (UWB) Technology

Abstract: Before the emergence of ultra-wideband (UWB) radios, widely used wireless communications were based on sinusoidal carriers, and impulse technologies were employed only in specific applications (e.g. radar). In 2002, the Federal Communication Commission (FCC) allowed unlicensed operation between 3.1–10.6 GHz for UWB communication, using a wideband signal format with a low EIRP level (−41.3dBm/MHz). UWB communication systems then emerged as an alternative to narrowband systems and significant effort in this area has been invested at the regulatory, commercial, and research levels.