arXiv subjects

Fu-Der Chen

Publications and source records attributed to Fu-Der Chen.

6 recordsLinked to original sources

arXiv2024

Implantable silicon neural probes with nanophotonic phased arrays for single-lobe beam steering

In brain activity mapping experiments using optogenetics, patterned illumination is crucial for deterministic and localized stimulation of neurons. However, due to optical scattering in brain tissue, light-emitting implantable devices are needed to bring precise patterned illumination to deep brain regions. A promising solution is silicon neural probes with integrated nanophotonic circuits that form tailored beam emission patterns without lenses. Here, we demonstrate neural probes with grating-based light emitters that generate a single steerable light beam across $> 60\%$ of the steering range with $\ge 4$ dB of background suppression for optogenetic photostimulation. The light emitters, optimized for blue or amber light, combine end-fire optical phased arrays with slab gratings to suppress higher-order sidelobes.

physics.optics↗

arXiv2024

Implantable Photonic Neural Probes with Out-of-Plane Focusing Grating Emitters

We have designed, fabricated, and characterized implantable silicon neural probes with nanophotonic grating emitters that focus the emitted light at a specified distance above the surface of the probe for spatially precise optogenetic targeting of neurons. Using the holographic principle, we designed gratings for wavelengths of 488 and 594 nm, targeting the excitation spectra of the optogenetic actuators Channelrhodopsin-2 and Chrimson, respectively. The measured optical emission pattern of these emitters in non-scattering medium and tissue matched well with simulations. To our knowledge, this is the first report of focused spots with the size scale of a neuron soma in brain tissue formed from implantable neural probes.

physics.optics↗

arXiv2023

Implantable Photonic Neural Probes with 3D-Printed Microfluidics and Applications to Uncaging

Advances in chip-scale photonic-electronic integration are enabling a new generation of foundry-manufacturable implantable silicon neural probes incorporating nanophotonic waveguides and microelectrodes for optogenetic stimulation and electrophysiological recording in neuroscience research. Further extending neural probe functionalities with integrated microfluidics is a direct approach to achieve neurochemical injection and sampling capabilities. In this work, we use two-photon polymerization 3D printing to integrate microfluidic channels onto photonic neural probes, which include silicon nitride nanophotonic waveguides and grating emitters. The customizability of 3D printing enables a unique geometry of microfluidics that conforms to the shape of each neural probe, enabling integration of microfluidics with a variety of existing neural probes while avoiding the complexities of monolithic microfluidics integration. We demonstrate the photonic and fluidic functionalities of the neural probes via fluorescein injection in agarose gel and photoloysis of caged fluorescein in solution and in flxed brain tissue.

physics.optics↗

arXiv2022

Microcantilever-integrated photonic circuits for broadband laser beam scanning

Laser beam scanning is central to many applications, including displays, microscopy, three-dimensional mapping, and quantum information. Reducing the scanners to microchip form factors has spurred the development of very-large-scale photonic integrated circuits of optical phased arrays and focal plane switched arrays. An outstanding challenge remains to simultaneously achieve a compact footprint, broad wavelength operation, and low power consumption. Here, we introduce a laser beam scanner that meets these requirements. Using microcantilevers embedded with silicon nitride nanophotonic circuitry, we demonstrate broadband, one- and two-dimensional steering of light with wavelengths from 410 nm to 700 nm. The microcantilevers have ultracompact ~0.1 mm$^2$ areas, consume ~31 to 46 mW of power, are simple to control, and emit a single light beam. The microcantilevers are monolithically integrated in an active photonic platform on 200-mm silicon wafers. The microcantilever-integrated photonic circuits miniaturize and simplify light projectors to enable versatile, power-efficient, and broadband laser scanner microchips.

physics.optics↗

arXiv2021

Optical phased array neural probes for beam-steering in brain tissue

Implantable silicon neural probes with integrated nanophotonic waveguides can deliver patterned dynamic illumination into brain tissue at depth. Here, we introduce neural probes with integrated optical phased arrays and demonstrate optical beam steering in vitro. Beam formation in brain tissue was simulated and characterized. The probes were used for optogenetic stimulation and calcium imaging.

physics.optics↗

arXiv2021

Self-supervised driven consistency training for annotation efficient histopathology image analysis

Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and Intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learn-ing unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data. We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression-based tasks, i.e., tumor metastasis detection, tissue type classification, and tumor cellularity quantification. Under limited-label data, the proposed method yields tangible improvements, which is close or even outperforming other state-of-the-art self-supervised and supervised baselines. Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks. Code and pretrained models will be made available at: https://github.com/srinidhiPY/SSL_CR_Histo

cs.CV↗