Enabling unique architectures and functionalities of microsystems for numerous applications in electronics, photonics and other areas often requires microassembly of separately prepared heterogeneous materials instead of monolithic microfabrication. However, microassembly of dissimilar materials while ensuring high structural integrity has been challenging in the context of deterministic transferring and joining of materials at the microscale where surface adhesion is far more dominant than body weight. Here we present an approach to assembling microsystems with microscale building blocks of four disparate classes of device-grade materials including semiconductors, metals, dielectrics, and polymers. This approach uniquely utilizes reversible adhesion-based transfer printing for material transferring and thermal processing for material joining at the microscale. The interfacial joining characteristics between materials assembled by this approach are systematically investigated upon different joining mechanisms using blister tests. The device level capabilities of this approach are further demonstrated through assembling and testing of a microtoroid resonator and a radio frequency (RF) microelectromechanical systems (MEMS) switch that involve optical and electrical functionalities with mechanical motion. This work opens up a unique route towards 3D heterogeneous material integration to fabricate microsystems.While monolithic microfabrication has been quite successful in the manufacturing of microsystems such as integrated circuits (IC) and microelectromechanical systems (MEMS) 1,2 , continued innovation towards three dimensional (3D) architectures and heterogeneous integration has been limited, which would otherwise enable improvements in performance and novel functionalities of microsystems. Associated challenges originate from layer-by-layer thin film processing on a single substrate and dissimilar nature of materials that may need different techniques to process. Consequently, 3D heterogeneous integration often requires independent fabrication of constituents followed by microassembly rather than monolithic microfabrication. In this context, transfer printing 3,4 has emerged as a method that utilizes highly reversible surface adhesion of a polymeric stamp to deterministically transfer microscale solid objects called "inks". The ability to transfer inks from a donor substrate where inks are grown and processed to a receiving substrate where inks are finally assembled reduces the complexity of manufacturing processes regarding heterogeneous material integration. Furthermore, previously reported micro-masonry 5 which relies on transfer printing demonstrates that after proper thermal processing, direct bonding between transferred silicon inks can be achieved, which may be sufficiently strong to produce various MEMS devices 6-8 . However, limited assembling material classes and quantitatively unknown interfacial characteristics between joined inks suppress broader adaptation of this transfer printing-based microasse...

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Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverage machine learning methods, to be specific, the recurrent neural network (RNN), to generate black-box macromodels and achieve significant reduction of computation time. Through the proposed approach, an RNN model is first trained and then validated on a relatively short sequence generated from a circuit simulator. Once the training completes, the RNN can be used to make predictions on the remaining sequence in order to generate an eye diagram. The training cost can also be amortized when the trained RNN starts making predictions. Besides, the proposed approach requires no complex circuit simulations nor substantial domain knowledge. We use two highspeed link examples to demonstrate that the proposed approach provides adequate accuracy while the computation time can be dramatically reduced. In the high-speed link example with a PAM4 driver, the eye diagram generated by RNN models shows good agreement with that obtained from a commercial circuit simulator. This paper also investigates the impacts of various RNN topologies, training schemes, and tunable parameters on both the accuracy and the generalization capability of an RNN model. It is found out that the long short-term memory (LSTM) network outperforms the vanilla RNN in terms of the accuracy in predicting transient waveforms.

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Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.