Liang Pan

Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization.

Microscale-oscillating heat pipes (micro-OHPs) have recently drawn interest for electronic cooling applications due to their compact size and passive operating mechanism. The occurrence of dryout in OHPs, however, at which the working liquid no longer wets the evaporator, limits the maximum operating cooling power, preventing their integration for direct cooling of high heat flux semiconductor chips. Here, we report on high power and high flux operation of silicon-based OHPs by using microchannels with hydraulic diameters of ∼200 μm. Particularly, a micro-OHP with 100 μm channel height is shown to effectively operate at 210 W using a dielectric working fluid, corresponding to an unprecedented cooling power density of 145 W/cm2, without dryout. A distinctive oscillating mode with highly periodic bulk circulations occurs at high heating power and can provide efficient heat dissipation. The flow speed of the liquid under this bulk circulation mode can be as high as 10 m/s. The empirical relationships between the heat transfer rate, oscillating frequency, and device temperatures are studied.

Scanning probe lithography (SPL) as a maskless approach with a low tool price can pattern a variety of materials at a nanometer or even atomic resolution. However, the throughput of conventional SPLs is extremely low due to their limited scanning speeds. Here, we report a high-speed, probe-based method to continuously pattern the substrate surface at a linear velocity of meters per second. We demonstrated direct writings of nanoscale patterns by using ultrafast electron-induced deposition inside a nanoscale flow at a patterning frequency of 20 MHz. The fast scan motion of the writing probe is precisely controlled by using self-adaptive hydro- and aerodynamics functions of a patterning head. The microscale electro-hydrodynamic ejection and microfluid channels are used to deliver the precursor at high scanning speeds. One patterning head can carry parallel probes to further enhance the patterning throughput. This low-cost, maskless patterning method opens new avenues to develop high-throughput nanomanufacturing techniques.

Additive manufacturing of sophisticated 3D nanoscale objects commonly uses femtosecond lasers to photopolymerize a light‐sensitive resin using multi‐photon absorption. This nonlinear process provides high accuracy and flexibility in advanced 3D fabrication; however, it typically has limited throughput and high cost. This work makes use of a one‐photon‐based dosage‐nonlinearity to fabricate 3D nanostructures, demonstrating a cost‐effective method for 3D nanolithography using a low‐cost 405 nm continuous‐wave diode laser. This dosage‐nonlinearity is achieved by using controlled depletion of photo‐initiation species in an environment containing inhibiting species. By controlling multiple competing processes, the undesired dose accumulation outside the writing voxel is stopped, and thereby this method creates a confined writing voxel in space. A multiphysics model is developed to numerically study the one‐photon nonlinear photopolymerization process, which is compared with experimental results.

There is demand for scaling up 3D printing throughput, especially for the multi-photon 3D printing process that provides sub-micrometer structuring capabilities required in diverse fields. In this work, high-speed projection multi-photon printing is combined with spatiotemporal focusing for fabrication of 3D structures in a rapid, layer-by-layer, and continuous manner. Spatiotemporal focusing confines printing to thin layers, thereby achieving print thicknesses on the micron and sub-micron scale. Through projection of dynamically varying patterns with no pause between patterns, a continuous fabrication process is established. A numerical model for computing spatiotemporal focusing and imaging is also presented which is verified by optical imaging and printing results. Complex 3D structures with smooth features are fabricated, with millimeter scale printing realized at a rate above 10⁻³ mm³ s⁻¹. This method is further scalable, indicating its potential to make fabrications of 3D structures with micro/nanoscale features in a practical time scale a reality.

Topology optimization for heat sink devices typically relies on penalization methods to ensure the final designs are composed of strictly solid or open regions. We formulate a homogenization approach wherein the partial densities are physically represented as porous microstructures. This formulation allows design of thermal management components that have sub-grid features that

Topology optimization for heat sink devices typically relies on penalization methods to ensure the final designs are composed of strictly solid or open regions. We formulate a homogenization approach wherein the partial densities are physically represented as porous microstructures. This formulation allows design of thermal management components that have sub-grid features that

Pluggable optoelectronic transceiver modules are widely used in the fiber-optic communication infrastructure. It is essential to mitigate thermal contact resistance between the high-power optical module and its riding heat sink in order to maintain the required operation temperature. The pluggable nature of the modules requires dry contact thermal interfaces that permit repeated insertion–disconnect cycles under low compression pressures (∼10 to 100 kPa). Conventional wet thermal interface materials (TIM), such as greases, or those that require high compression pressures, are not suitable for pluggable operation. Here, we demonstrate the use of compliant microstructured TIM to enhance the thermal contact conductance between an optical module and its riding heat sink under a low compression pressure (20 kPa). The metallized and polymer-coated structures are able to accommodate the surface nonflatness and microscale roughness of the mating surface while maintaining a high effective thermal conductance across the thickness. This dry contact TIM is demonstrated to maintain reliable thermal performance after 100 plug-in and plug-out cycles while under compression.