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Gayangana Leelarathne

Publications and source records attributed to Gayangana Leelarathne.

2 recordsLinked to original sources

A 2.4 GHz LC-VCO Fractional-N Phase Locked Loop Open-Source Design in 130-nm BiCMOS

Radio frequency (RF) integrated circuit design using the open-source complementary Metal-Oxide semiconductor (CMOS) ecosystem, such as for phase-locked loops (PLLs), is limited by the absence of reliable passive device models, particularly on-chip spiral inductors. Consequently, prior work relies on ring-oscillator-based voltage-controlled oscillators (VCOs) with degraded phase noise performance. This work presents a 2.4 GHz type-II fractional-N PLL implemented in the IHP SG13G2 130 nm BiCMOS open-source technology. The proposed design employs a cross-coupled differential LC-VCO integrated with a custom-designed spiral inductor, developed using an open-source electromagnetic modelling workflow in OpenEMS. The optimized inductor achieves 4 nH inductance with a quality factor of 16.8 at 2.45 GHz. The LC-VCO sensitivity is approximately 120 MHz/V, while the PLL phase noise is -100.8 dBc/Hz at 1 MHz offset. The complete PLL is realized using a fully open-source electronic design automation (EDA) flow, occupying a total area of 930 um x 666 um (~0.619 mm2) and consuming 12.73 mW, demonstrating the feasibility of RF integrated circuit design in an open-source CMOS IC design ecosystem.

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Vision Transformer with Convolutional Encoder-Decoder for Hand Gesture Recognition using 24 GHz Doppler Radar

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. We propose a vision-transformer-based architecture for HGR with multi-antenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: a convolutional encoderdecoder, an attention module with three transformer layers, and a multi-layer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.

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