Scalable high-precision trimming of photonic resonances by polymer exposure to energetic beams

Integrated photonic circuits (PICs) have seen an explosion in interest, through to commercialization in the past decade. Most PICs rely on sharp resonances to modulate, steer, and multiplex signals. However, the spectral characteristics of high-quality resonances are highly sensitive to small variations in fabrication and material constants, which limits their applicability. Active tuning mechanisms are commonly employed to account for such deviations, consuming energy and occupying valuable chip real estate. Readily employable, accurate, and highly scalable mechanisms to tailor the modal properties of photonic integrated circuits are urgently required. Here, we present an elegant and powerful solution to achieve this in a scalable manner during the semiconductor fabrication process using existing lithography tools: by exploiting the volume shrinkage exhibited by certain polymers to permanently modulate the waveguide’s effective index. This technique enables broadband and lossless tuning with immediate applicability in wide-ranging applications in optical computing, telecommunications, and free-space optics

Chalcogenide phase-change devices for neuromorphic photonic computing

The integration of artificial intelligence systems into daily applications like speech recognition and autonomous driving rapidly increases the amount of data generated and processed. However, satisfying the hardware requirements with the conventional von Neumann architecture remains challenging due to the von Neumann bottleneck. Therefore, new architectures inspired by the working principles of the human brain are developed, and they are called neuromorphic computing. The key principles of neuromorphic computing are in-memory computing to reduce data shuffling and parallelization to decrease computation time. One promising framework for neuromorphic computing is phase-change photonics. By switching to the optical domain, parallelization is inherently possible by wavelength division multiplexing, and high modulation speeds can be deployed. Non-volatile phase-change materials are used to perform multiplications and non-linear operations in an energetically efficient manner. Here, we present two prototypes of neuromorphic photonic computation units based on chalcogenide phase-change materials. First is a neuromorphic hardware accelerator designed to carry out matrix vector multiplication in convolutional neural networks. Due to the neuromorphic architecture, this prototype can already operate at tera-multiply-accumulate per second speeds. Second is an all-optical spiking neuron, which can serve as a building block for large-scale artificial neural networks. Here, the whole computation is carried out in the optical domain, and the device only needs an electrical interface for data input and readout

Integrated optical pattern generation on thin-film lithium niobate with electro-optic modulators and phase-change material cells

Reconfigurable photonic integrated circuits enable high-bandwidth signal shaping with the prospect for scalability and compact footprint. Co-integration of electro-optical tunability with non-volatile attenuation through functional materials allows for implementing photonic devices which operate both on phase and amplitude. Based on this approach, we propose an integrated photonic design for optical pattern generation deploying a continuous-wave laser and a single electrical function generator. We employ the non-volatile and reconfigurable phase-change material Ge2Sb2Te5 (GST) as a tunable attenuator for an integrated photonic circuit on the Lithium-Niobate-On-Insulator (LNOI) platform. The GST can be switched between its amorphous and crystalline phase, leading to an optical contrast of ≅18 dB. Combining this with integrated electro-optical modulators with a 4 GHz bandwidth in LNOI, enables the generation of short optical pulses, based on the principles of inverse discrete Fourier transform

Integrated all-photonic non-volatile multi-level memory

Implementing on-chip non-volatile photonic memories has been a long-term, yet elusive goal. Photonic data storage would dramatically improve performance in existing computing architectures1 by reducing the latencies associated with electrical memories2 and potentially eliminating optoelectronic conversions3. Furthermore, multi-level photonic memories with random access would allow for leveraging even greater computational capability4, 5, 6. However, photonic memories3, 7, 8, 9, 10 have thus far been volatile. Here, we demonstrate a robust, non-volatile, all-photonic memory based on phase-change materials. By using optical near-field effects, we realize bit storage of up to eight levels in a single device that readily switches between intermediate states. Our on-chip memory cells feature single-shot readout and switching energies as low as 13.4 pJ at speeds approaching 1 GHz. We show that individual memory elements can be addressed using a wavelength multiplexing scheme. Our multi-level, multi-bit devices provide a pathway towards eliminating the von Neumann bottleneck and portend a new paradigm in all-photonic memory and non-conventional computing.Deutsche Forschungsgemeinschaft (DFG)Engineering and Physical Sciences Research Council (EPSRC)JEOL UKClarendon FundKarlsruhe School of Optics and Photonics (KSOP)Stiftung der Deutschen Wirtschaft (sdw)John Fell FundDFGState of Baden-WürttembergDFG-Center for Functional Nanostructures (CFN