In integrated photonics, specific wavelengths such as 1,550 nm are preferred due to low-loss transmission and the availability of optical gain in this spectral region. For chip-based photodetectors, two-dimensional materials bear scientifically and technologically relevant properties such as electrostatic tunability and strong light–matter interactions. However, no efficient photodetector in the telecommunication C-band has been realized with two-dimensional transition metal dichalcogenide materials due to their large optical bandgaps. Here we demonstrate a MoTe₂-based photodetector featuring a strong photoresponse (responsivity 0.5 A W^–1) operating at 1,550 nm in silicon photonics enabled by strain engineering the two-dimensional material. Non-planarized waveguide structures show a bandgap modulation of 0.2 eV, resulting in a large photoresponse in an otherwise photoinactive medium when unstrained. Unlike graphene-based photodetectors that rely on a gapless band structure, this photodetector shows an approximately 100-fold reduction in dark current, enabling an efficient noise-equivalent power of 90 pW Hz^–0.5. Such a strain-engineered integrated photodetector provides new opportunities for integrated optoelectronic systems.

Machine intelligence has become a driving factor in modern society. However, its demand outpaces the underlying electronic technology due to limitations given by fundamental physics, such as capacitive charging of wires, but also by system architecture of storing and handling data, both driving recent trends toward processor heterogeneity. Task-specific accelerators based on free-space optics bear fundamental homomorphism for massively parallel and real-time information processing given the wave nature of light. However, initial results are frustrated by data handling challenges and slow optical programmability. Here we introduce a novel amplitude-only Fourier-optical processor paradigm capable of processing large-scale ∼ ( 1000 × 1000 ) matrices in a single time step and 100 µs-short latency. Conceptually, the information flow direction is orthogonal to the two-dimensional programmable network, which leverages 10 6 parallel channels of display technology, and enables a prototype demonstration performing convolutions as pixelwise multiplications in the Fourier domain reaching peta operations per second throughputs. The required real-to-Fourier domain transformations are performed passively by optical lenses at zero-static power. We exemplary realize a convolutional neural network (CNN) performing classification tasks on 2 megapixel large matrices at 10 kHz rates, which latency-outperforms current graphic processing unit and phase-based display technology by 1 and 2 orders of magnitude, respectively. Training this optical convolutional layer on image classification tasks and utilizing it in a hybrid optical-electronic CNN, shows classification accuracy of 98% (Modified National Institute of Standards and Technology) and 54% (CIFAR-10). Interestingly, the amplitude-only CNN is inherently robust against coherence noise in contrast to phase-based paradigms and features a delay over 2 orders of magnitude lower than liquid-crystal-based systems. Such an amplitude-only massively parallel optical compute paradigm shows that the lack of phase information can be accounted for via training, thus opening opportunities for high-throughput accelerator technology for machine intelligence with applications in network-edge processing, in data centers, or in pre-processing information or filtering toward near-real-time decision making.

With an ongoing trend in computing hardware toward increased heterogeneity, domain-specific coprocessors are emerging as alternatives to centralized paradigms. The tensor core unit has been shown to outperform graphic processing units by almost 3 orders of magnitude, enabled by a stronger signal and greater energy efficiency. In this context, photons bear several synergistic physical properties while phase-change materials allow for local nonvolatile mnemonic functionality in these emerging distributed non-von Neumann architectures. While several photonic neural network designs have been explored, a photonic tensor core to perform tensor operations is yet to be implemented. In this manuscript, we introduce an integrated photonics-based tensor core unit by strategically utilizing (i) photonic parallelism via wavelength division multiplexing, (ii) high 2 peta-operations-per-second throughputs enabled by tens of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and (iii) near-zero static power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show, supported by numerical simulations, that the performance of this 4-bit photonic tensor core unit can be 1 order of magnitude higher for electrical data. The full potential of this photonic tensor processor is delivered for optical data being processed, where we find a 2–3 orders higher performance (operations per joule), as compared to an electrical tensor core unit, while featuring similar chip areas. This work shows that photonic specialized processors have the potential to augment electronic systems and may perform exceptionally well in network-edge devices in the looming 5G networks and beyond.

Polaritons with a twist: The ability to design and fabricate optical systems with tunable topological features makes them especially attractive for developing analogs of topological condensed matter systems, which by themselves tend to be fixed or limited in their tunability. Liu et al. now show that the combination of a two-dimensional material with a photonic crystal can be used to develop an analogous quantum spin Hall system. The strong coupling between the monolayer tungsten disulfide excitons with a nontrivial hexagonal photonic crystal gives rise to helical topological polaritons observed at up to 200 kelvin. The topological polaritons can be actively tuned by temperature and may further be manipulated with electric or magnetic fields, thereby providing a flexible platform with which to explore exotic topological phenomena and new phases of quantum matter. Science , this issue p. 600