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Mau-Chung Frank Chang

Researcher at University of California, Los Angeles

Publications -  356
Citations -  7957

Mau-Chung Frank Chang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: CMOS & Heterojunction bipolar transistor. The author has an hindex of 42, co-authored 345 publications receiving 7398 citations. Previous affiliations of Mau-Chung Frank Chang include Rockwell Automation & West Virginia University.

Papers
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Journal ArticleDOI

A Blocker-Tolerant, Noise-Cancelling Receiver Suitable for Wideband Wireless Applications

TL;DR: A new wideband receiver architecture is proposed that employs two separate passive-mixer-based downconversion paths, which enables noise cancelling, but avoids voltage gain at blocker frequencies.
Proceedings ArticleDOI

CMP network-on-chip overlaid with multi-band RF-interconnect

TL;DR: This paper explores the use of multi-band radio frequency interconnect (or RF-I) with signal propagation at the speed of light to provide shortcuts in a many core network-on-chip (NoC) mesh topology, and investigates the costs associated with this technology, and examines the latency and bandwidth benefits that it can provide.
Journal ArticleDOI

GaAlAs/GaAs heterojunction bipolar transistors: issues and prospects for application

TL;DR: The microwave and digital performance status of GaAlAs/GaAs heterojunction bipolar transistors (HBTs) is reviewed in this paper, where the maximum frequency of oscillation above 200 GHz and frequency divider operation at 26.9 GHz are reported.
Patent

Monolithically integrated switched capacitor bank using micro electro mechanical system (MEMS) technology

TL;DR: In this paper, a monolithically integrated switched capacitor bank using MEMS technology that is capable of handling GHz signal frequencies in both the RF and millimeter bands while maintaining precise digital selection of capacitor levels over a wide tuning range is presented.
Patent

Wireless wearable big data brain machine interface

TL;DR: In this paper, a wireless wearable high data throughput (big data) brain machine interface apparatus is presented, where an implanted recording and transmitting module collects neural data from a plurality of implanted electrodes and wirelessly transmits this over a short distance to a wearable (not implanted) receiving and forwarding module, which communicates the data over a wired communication to a mobile post processing device.