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Backside Power Delivery Network (BSPDN) is a crucial technology for integrated circuits at sub-3 nm technology nodes. The primary challenge resides in utilizing nano through silicon via (nano-TSV) to establish connections between the backside power network and buried power rails, thereby facilitating transistor powering. The key technology is to ensure a smooth sidewall morphology and prevent damage to buried power rails (BPR) due to over-etching. In this study, non-Bosch and Bosch techniques are compared using simulation. The results demonstrate that while the non-Bosch technique yields smooth sidewalls, it inevitably leads to over-etching, whereas Bosch effectively avoids over-etching. The etching of scallop-free nano-TSV is achieved by optimizing the Bosch process, which involves the use of inductively coupled plasma (ICP). Finally, metal filling of nano-TSV is successfully achieved. Thus, the nano-TSV etching method is established as viable for BSPDN. [Display omitted] • A nano-TSV etching method for BSPDN is established that achieves smooth sidewalls and no over-etching. • The manuscript compares the etching mechanism of non-Bosch and Bosch. • The etching method is compatible with existing processes. [ABSTRACT FROM AUTHOR]

Fabrication imperfections strongly influence the functioning of Micro-Electro-Mechanical Systems (MEMS) if not taken into account during the design process. They must be indeed identified or precisely predicted to guarantee a proper compensation during the calibration phase or directly in operation. In this work, we propose an efficient approach for the identification of geometric uncertainties of MEMS, exploiting the asymptotic homogenization technique. In particular, the proposed strategy is experimentally validated on a MEMS filter, a device constituted by a complex periodic geometry, which would require high computational costs if simulated through full-order models. The complex periodic structure is replaced by an equivalent homogeneous medium, allowing a fast optimization procedure to identify imperfections by comparing a simplified analytical model with the experimental data available for the MEMS filter. The actual over-etch, obtained after the release phase, and the electrode offset of a fabricated MEMS filter are effectively identified through the proposed strategy. [ABSTRACT FROM AUTHOR]

In this work, we provide a numerical/experimental investigation of the micromechanics-induced scattered response of a polysilicon on-chip MEMS testing device, whose moving structure is constituted by a slender cantilever supporting a massive perforated plate. The geometry of the cantilever was specifically designed to emphasize the micromechanical effects, in compliance with the process constraints. To assess the effects of the variability of polysilicon morphology and of geometrical imperfections on the experimentally observed nonlinear sensor response, we adopt statistical Monte Carlo analyses resting on a coupled electromechanical finite element model of the device. For each analysis, the polysilicon morphology was digitally built through a Voronoi tessellation of the moving structure, whose geometry was in turn varied by sampling out of a uniform probability density function the value of the over-etch, considered as the main source of geometrical imperfections. The comparison between the statistics of numerical and experimental results is adopted to assess the relative significance of the uncertainties linked to variations in the micro-fabrication process, and the mechanical film properties due to the polysilicon morphology. [ABSTRACT FROM AUTHOR]

In this work, we provide a numerical/experimental investigation of the micromechanics-induced scattered response of a polysilicon on-chip MEMS testing device, whose moving structure is constituted by a slender cantilever supporting a massive perforated plate. The geometry of the cantilever was specifically designed to emphasize the micromechanical effects, in compliance with the process constraints. To assess the effects of the variability of polysilicon morphology and of geometrical imperfections on the experimentally observed nonlinear sensor response, we adopt statistical Monte Carlo analyses resting on a coupled electromechanical finite element model of the device. For each analysis, the polysilicon morphology was digitally built through a Voronoi tessellation of the moving structure, whose geometry was in turn varied by sampling out of a uniform probability density function the value of the over-etch, considered as the main source of geometrical imperfections. The comparison between the statistics of numerical and experimental results is adopted to assess the relative significance of the uncertainties linked to variations in the micro-fabrication process, and the mechanical film properties due to the polysilicon morphology.