A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS

The path towards miniaturization for micro-electro-mechanical systems (MEMS) has recently increased the effects of stochastic variability at the (sub)micron scale on the overall performance of the devices. We recently proposed and designed an on-chip testing device to characterize two sources of variability that majorly affect the scattering in response to the external actions of inertial (statically determinate) micromachines: the morphology of the polysilicon film constituting the movable parts of the device, and the environment-affected over-etch linked to the microfabrication process. A fully stochastic model of the entire device has been set to account for these two sources on the measurable response of the devices, e.g., in terms of the relevant C-V curves up to pull-in. A complexity in the mentioned model is represented by the need to assess the stochastic (local) stiffness of polysilicon, depending on its unknown (local) microstructure. In this work, we discuss a deep learning approach to the micromechanical characterization of polysilicon films, based on densely connected neural networks (NNs). Such NNs extract relevant features of the polysilicon morphology from SEM-like Voronoi tessellation-based digital microstructures. The NN-based model or surrogate is shown to correctly catch size effects at a varying ratio between the characteristic size of the structural components of the device, and the morphology-induced length scale of the aggregate of silicon grains. This property of the model looks to indeed be necessary to prove the generalization capability of the learning process, and to next feed Monte Carlo simulations resting on the model of the entire device.

Fabricación y caracterización de películas transparentes y conductoras utilizadas como contactos en estructuras MIM con películas SRO-LPCVD y SRO-HFCVD sobre sustrato de cuarzo

This work presents the manufacturing and characterization process of two types of transparent conductive oxide (TCO) films, as contacts. Aluminum doped zinc oxide (AZO) deposited with the Sputtering technique and tin doped indium oxide (ITO) using the pyrolysis spray technique, these transparent and conductive films were deposited as contacts on a film of siliconrich oxide (SRO) which was deposited by two systems of chemical vapor deposition by low-pressure (LPCVD) and by hotfilament (HFCVD) on quartz substrates with polysilicon film as metal contact, with the object of building two Metal-InsulatingMetal (MIM) structures, one with SRO-LPCVD film and another SRO-HFCVD thus highlighting the electrical characteristics of these structures. The precursors used for the LPCVD system are silane (SiH4) and nitrous oxide (N2O) and for the HFCVD system the gaseous precursors are obtained from a solid quartz source stripped with atomic hydrogen. First, we present results of the optical characterizations of the TCO´s and SRO films, the band gap obtained by Tauc to calculate the size of the nanocrystal in SRO-films, causing light spots. And I-V curves of MIM structures with interesting results.

Влияние условий формирования геттера в высокоомном кремнии на характеристики PIN-фотодиодов

The influence of gettering conditions in high resistivity silicon during the PIN photodiode fabrication process on the reverse dark currents has been studied. It was demonstrated that the getter formation of backside substrate by a combination of phosphorus ion implantation and deposition of polysilicon film followed by phosphorus doping at the temperatures below 900 0C results in reduction of reverse dark current value and increasing of nonequilibrium carrier lifetime.

Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach

Deep Learning strategies recently emerged as powerful tools for the characterization of heterogeneous materials. In this work, we discuss an approach for the characterization of the mechanical response of polysilicon films that typically constitute the movable structures of micro-electro-mechanical systems (MEMS). A dataset of microstructures is digitally generated and a neural network is trained to provide the appropriate scattering in the values of the overall stiffness (in terms of the Young’s modulus) of the grain aggregate. Since results are framed within a stochastic procedure, the aim of the learning strategy is not to accurately reproduce the microstructure-informed response of the polysilicon film, but instead to provide a fast tool to be used at the device level for Monte Carlo analysis of the relevant performance indices. Accuracy of the proposed approach is assessed for very small samples of the polycrystalline aggregate to check if size effects are correctly captured.