Influence of Si wafer thinning processes on (sub)surface defects

Wafer-to-wafer three-dimensional (3D) integration with minimal Si thickness can produce interacting multiple devices with significantly scaled vertical interconnections. Realizing such a thin 3D structure, however, depends critically on the surface and subsurface of the remaining backside Si after the thinning processes. The Si (sub)surface after mechanical grinding has already been characterized fruitfully for a range of few dozen of mu m. Here, we expand the characterization of Si (sub)surface to 5 mu m thickness after thinning process on dielectric bonded wafers. The subsurface defects and damage layer were investigated after grinding, chemical mechanical polishing (CMP), wet etching and plasma dry etching. The (sub)surface defects were characterized using transmission microscopy, atomic force microscopy, and positron annihilation spectroscopy. Although grinding provides the fastest removal rate of Si, the surface roughness was not compatible with subsequent processing. Furthermore, mechanical damage such as dislocations and amorphous Si cannot be reduced regardless of Si thickness and thin wafer handling systems. The CMP after grinding showed excellent performance to remove this grinding damage, even though the removal amount is 1 mu m. For the case of Si thinning towards 5 pin using grinding and CMP, the (sub)surface is atomic scale of roughness without vacancy. For the case of grinding + dry etch, vacancy defects were detected in subsurface around 0.5-2 mu m. The finished surface after wet etch remains in the nm scale in the strain region. By inserting a CMP step in between grinding and dry etch it is possible to significantly reduce not only the roughness, but also the remaining vacancies at the subsurface. The surface of grinding+ CMP + dry etching gives an equivalent mono vacancy result as to that of grinding + CMP. This combination of thinning processes allows development of extremely thin 3D integration devices with minimal roughness and vacancy surface. (C) 2017 Elsevier B.V. All rights reserved.