Core Technical Bottlenecks of Semi-Automatic Wafer Thinning Machines-

Core Technical Bottlenecks of Semi-Automatic Wafer Thinning Machines

2025-12-181020

Ultra-Thin Wafer Handling Dilemma: When processing ultra-thin wafers below 50μm, Semi-Automatic Wafer Thinning Machines face a sharp decline in wafer rigidity. Traditional vacuum chucking methods struggle to balance the needs of secure fixation and damage prevention. This results in a yield rate of only around 85% for 12-inch ultra-thin wafer thinning, with frequent warping and chipping issues, becoming a key pain point restricting equipment application.
Conflict Between Precision and Stress Control: During the mechanical grinding process of Semi-Automatic Wafer Thinning Machines, the mismatch in thermal expansion coefficients between the grinding wheel and the wafer easily induces uneven thermal deformation, leading to a total thickness variation (TTV) exceeding 2μm. Meanwhile, for brittle materials such as SiC and GaN, the equipment cannot precisely control the grinding force, which tends to cause edge chipping and subsurface cracks, with the damaged layer depth reaching 2–10μm.
Limitations in Heterogeneous Material Adaptation: In 3D IC packaging scenarios, there is a significant hardness difference between the TSV copper interconnection layer and the silicon substrate (2.5GPa vs 12GPa). The single grinding mode of Semi-Automatic Wafer Thinning Machines cannot adapt to such material differences, resulting in grinding force fluctuations that seriously affect wafer surface quality. The special physical properties of wide bandgap semiconductors further exacerbate the equipment’s adaptation difficulty.
Efficiency Bottleneck of Semi-Automatic Processes: Existing Semi-Automatic Wafer Thinning Machines rely on manual-assisted loading and unloading, as well as manual adjustment of process parameters. This not only makes the single-wafer thinning cycle 30% longer than that of fully automatic equipment, but also causes poor batch consistency due to differences in manual operations. The high dependence on operator experience limits productivity improvement and large-scale application.

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