Benefiting from ultrafast temporal resolution, broadband spectral bandwidth, as well as high peak power, passively mode-locked fibre lasers have attracted growing interest and exhibited great potential from fundamental sciences to industrial and military applications. As a nonlinear system containing complex interactions from gain, loss, nonlinearity, dispersion, etc., ultrafast fibre lasers deliver not only conventional single soliton but also soliton bunching with different types. In analogy to molecules consisting of several atoms in chemistry, soliton molecules (in other words, bound solitons) in fibre lasers are of vital importance for in-depth understanding of the nonlinear interaction mechanism and further exploration for high-capacity fibre-optic communications. In this Review, we summarize the state-of-the-art advances on soliton molecules in ultrafast fibre lasers. A variety of soliton molecules with different numbers of soliton, phase-differences and pulse separations were experimentally observed owing to the flexibility of parameters such as mode-locking techniques and dispersion control. Numerical simulations clearly unravel how different nonlinear interactions contribute to formation of soliton molecules. Analysis of the stability and the underlying physical mechanisms of bound solitons bring important insights to this field. For a complete view of nonlinear optical phenomena in fibre lasers, other dissipative states such as vibrating soliton pairs, soliton rains, rogue waves and coexisting dissipative solitons are also discussed. With development of advanced real-time detection techniques, the internal motion of different pulsing states is anticipated to be characterized, rendering fibre lasers a versatile platform for nonlinear complex dynamics and various practical applications.

Compared to conventional lasers limited to generating static modes, mode-switchable lasers equipped with adjustable optics significantly enhance the flexibility and versatility of coherent light sources. However, most current approaches to achieving mode-switchable lasers depend on conventional, i.e., inherently bulky and slow, optical components. Here, we demonstrate fiber lasers empowered by electrically actuated intracavity microelectromechanical system (MEMS)–based optical metasurface (MEMS-OMS) enabling mode switching between fundamental Gaussian and vortex modes at ~1030 nm. By finely adjusting the voltage applied to the MEMS mirror, high-contrast switching between Gaussian ( l = 0) and vortex ( l = 1, 2, 3, and 5, depending on the OMS arrangement) laser modes is achieved, featuring high mode purities (>95%) and fast responses (~100 microseconds). The proposed intracavity MEMS-OMS–enabled laser configuration provides an at-source solution for generating high-purity fast-switchable laser modes, with potential applications ranging from advanced optical imaging to optical tweezers, optical machining, and intelligent photonics.

Chiral plasmonic metasurfaces are promising for enlarging the chiral signals of biomolecules and improving the sensitivity of bio-sensing. However, the design process of the chiral plasmonic nanostructures is time consuming. Deep learning has been playing a key role in the design of photonic devices with high time efficiency and good design performance. This paper proposes a deep neural network (DNN) to achieve forward prediction and inverse design for 3D chiral plasmonic metasurfaces, and further improve the training speed and performance by the transfer learning method. Once the DNNs are trained using a part of the sampled data from the parameter space, the circular dichroism (CD) spectra can be predicted within the time on milliseconds (about 3.9 ms for forward network and 5.6 ms for inverse network) with high prediction accuracy. The inverse design was optimized by taking more spectral information into account and extracting the critical features using the one-dimensional convolutional kernel. The aforementioned trained network for one handedness can accelerate the training speed and improve performance with small datasets for the opposite handedness via the transfer learning method. The proposed approach is instructive in the design process of chiral plasmonic metasurfaces and could find applications in exploring versatile complex nanophotonic devices efficiently.