arXiv subjects

Alexander J. Geers

Publications and source records attributed to Alexander J. Geers.

3 recordsLinked to original sources

arXiv2025

A single chip 1.024 Tb/s silicon photonics PAM4 receiver

Energy-efficient high-bandwidth interconnects play a key role in computing systems. Advances in silicon photonic electro-optic modulators and wavelength selective components have enabled the utilization of wavelength-division-multiplexing (WDM) in integrated optical transceivers, offering a high data-rate operation while achieving enhanced energy efficiency, bandwidth density, scalability, and the reach required for data-centers. Here, we report the demonstration of a single chip optical WDM PAM4 receiver, where by co-integration of a 32-channel optical demultiplexer (O-DeMux) with autonomous wavelength tuning and locking at a near-zero power consumption and a 32-channel ultra-low power concurrent electrical detection system, a record chip energy efficiency of under 0.38 pJ/bit is measured. The implemented 32 channel monolithic WDM optical receiver chip achieves an end-to-end latency of under 100 ps and a bit-error-rate of less than 10-12 with no equalization, pre-distortion, or digital-signal-processing, while operating at 1.024 Tb/s aggregate data-rate on a single input fiber, the largest reported data-rate for a WDM PAM4 receiver chip to date. The receiver bandwidth density of more than 3.55 Tb/s/mm2 corresponds to more than an order-of-magnitude larger bandwidth density-energy efficiency product compared to the state-of-the-art optical PAM4 receivers for beyond 100Gb/s links. The chip, integrated using GlobalFoundries 45CLO CMOS-photonic process, can be used for implementation of energy-efficient high data-rate optical links for AI applications.

physics.optics↗

arXiv2023

Frequency Tunable Magnetostatic Wave Filters With Zero Static Power Magnetic Biasing Circuitry

A single tunable filter simplifies complexity, reduces insertion loss, and minimizes size compared to frequency switchable filter banks commonly used for radio frequency (RF) band selection. Magnetostatic wave (MSW) filters stand out for their wide, continuous frequency tuning and high-quality factor. However, MSW filters employing electromagnets for tuning consume excessive power and space, unsuitable for consumer wireless applications. Here, we demonstrate miniature and high selectivity MSW tunable filters with zero static power consumption, occupying less than 2 cc. The center frequency is continuously tunable from 3.4 GHz to 11.1 GHz via current pulses of sub-millisecond duration applied to a small and nonvolatile magnetic bias assembly. This assembly is limited in the area over which it can achieve a large and uniform magnetic field, necessitating filters realized from small resonant cavities micromachined in thin films of Yttrium Iron Garnet. Filter insertion loss of 3.2 dB to 5.1 dB and out-of-band third order input intercept point greater than 41 dBm are achieved. The filter's broad frequency range, compact size, low insertion loss, high out-of-band linearity, and zero static power consumption are essential for protecting RF transceivers and antennas from interference, thus facilitating their use in mobile applications like IoT and 6G networks.

physics.app-ph↗

arXiv2021

Single-chip photonic deep neural network for instantaneous image classification

Deep neural networks with applications from computer vision and image processing to medical diagnosis are commonly implemented using clock-based processors, where computation speed is limited by the clock frequency and the memory access time. Advances in photonic integrated circuits have enabled research in photonic computation, where, despite excellent features such as fast linear computation, no integrated photonic deep network has been demonstrated to date due to the lack of scalable nonlinear functionality and the loss of photonic devices, making scalability to a large number of layers challenging. Here we report the first integrated end-to-end photonic deep neural network (PDNN) that performs instantaneous image classification through direct processing of optical waves. Images are formed on the input pixels and optical waves are coupled into nanophotonic waveguides and processed as the light propagates through layers of neurons on-chip. Each neuron generates an optical output from input optical signals, where linear computation is performed optically and the nonlinear activation function is realised opto-electronically. The output of a laser coupled into the chip is uniformly distributed among all neurons within the network providing the same per-neuron supply light. Thus, all neurons have the same optical output range enabling scalability to deep networks with large number of layers. The PDNN chip is used for 2- and 4-class classification of handwritten letters achieving accuracies of higher than 93.7% and 90.3%, respectively, with a computation time less than one clock cycle of state-of-the-art digital computation platforms. Direct clock-less processing of optical data eliminates photo-detection, A/D conversion, and the requirement for a large memory module, enabling significantly faster and more energy-efficient neural networks for the next generations of deep learning systems.

cs.ET↗