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  "title": "Plasma and wet etch: selectivity and profile",
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  "description": "Removes selected material while preserving the feature and surrounding stack.",
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      "title": "Plasma and wet etch: selectivity and profile",
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      "title": "Explore in Atlas",
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      "title": "Lam Research — Our Processes",
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      "title": "MIT 6.152J — Micro/Nano Processing Technology lecture notes",
      "url": "https://ocw.mit.edu/courses/6-152j-micro-nano-processing-technology-fall-2005/pages/lecture-notes/"
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      "title": "A process chamber as a controlled reactor",
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  "content": "Fabrication / Repeated operations · Atlas v1.3.0\n\n# Plasma and wet etch: selectivity and profile\n\nRemoves selected material while preserving the feature and surrounding stack.\n\n[Explore in Atlas](https://semiaifoundry.com/ai-atlas/#plasma-wet-etch-transfer)Optical field to nanometer pattern; material states distinguished\n\nLearning objectiveSeparate etch selectivity, directionality, endpoint and damage.\n\n## The mechanism\n\nEtching converts temporary masking geometry into a material feature. Wet etching relies on reactions in a liquid and can depend strongly on crystal orientation and chemistry. Plasma etching combines reactive species with energetic particle effects that can help control directionality. Neither is simply “cutting downward”: sidewall reactions, transport, charging and mask erosion influence the result. Selectivity compares removal of the target material with loss of another exposed material. A successful transfer must satisfy depth, profile, roughness, residue and damage requirements together. This explains why an acceptable resist image does not automatically become an acceptable transistor or via.\n\n1.  Identify the exposed target, mask and stop/protected materials before choosing the route.\n2.  Deliver reactive species and, for a plasma route, control the relevant energetic interactions.\n3.  Remove reaction products while transport changes along a deepening feature.\n4.  Monitor endpoint or timing while allowing for wafer and pattern-density variation.\n5.  Measure depth, sidewall profile, residues and damage after transfer and cleaning.\n\n## Science and assumptions\n\n### Mask-consumption estimate\n\n$$\n\\Delta h_{\\mathrm{mask}}\\approx\\frac{h_{\\mathrm{target}}}{S}\n$$\n\nMask thickness consumed is approximately target etched depth divided by target-to-mask selectivity.\n\nSymbols and units\n\nΔh\\_mask\n\nMask thickness consumed\n\nnm\n\nh\\_target\n\nTarget material etched depth\n\nnm\n\nS\n\nTarget-to-mask etch-rate selectivity\n\ndimensionless rate ratio\n\n$h_{\\mathrm{target}}$ is etched target depth and S target-to-mask selectivity; lengths use matching units.\n\nAssumptions Constant-rate approximation without loading, faceting, endpoint overetch or spatial variation; use it only as an initial material-budget check.\n\n## Engineering example\n\nSynthetic budget: etching 200 nm with target-to-mask selectivity 10 consumes about 20 nm of mask in the simple model. A 20 nm starting mask has no allowance for nonuniformity or overetch.\n\n## Distinctions and tradeoffs\n\nHigh selectivity is not the same as vertical sidewalls or low defectivity.\n\nMore directional or faster removal can trade against selectivity, damage and uniformity; the best compromise depends on the complete stack.\n\nFin shapes, contacts, vias and TSVs rely on accurate material removal that leaves neighboring structures electrically and mechanically usable.\n\n## Specifications and interfaces\n\nSelectivity\n\nTarget removal rate / protected-material removal rate\n\nDimensionless ratio measured under the same process conditions.\n\nProfile\n\nDepth, sidewall angle and bottom shape\n\nName the material boundary and measurement stage.\n\n### Interfaces\n\nPatterned mask and stack Input\n\nExposed materials, feature geometry and initial surface state\n\nTransferred structure Output\n\nEtched profile, residues and possible surface damage\n\n## Check your understanding\n\n### A process preserves the mask well but leaves tapered holes. Which property is still inadequate?\n\n1.  Atomic number of silicon\n2.  Only the test software\n3.  Profile control, despite acceptable mask selectivity\n\nAnswer and reasoning\n\nProfile control, despite acceptable mask selectivity\n\nSelectivity compares material removal; it does not uniquely determine the spatial removal profile.\n\n## Evidence and scope\n\nNo plasma settings or wet-chemical composition is given. Schematic etch routes are not production recipes or proof of a defect cause.\n\nUse the manufacturing investigation to trace compatible inputs, assembly and operating constraints; no current standalone calculator models this lesson’s full mechanism.\n\n## Sources\n\n-   [Lam Research — Our Processes](https://www.lamresearch.com/products/our-processes/)\n-   [MIT 6.152J — Micro/Nano Processing Technology lecture notes](https://ocw.mit.edu/courses/6-152j-micro-nano-processing-technology-fall-2005/pages/lecture-notes/)\n-   [Applied Materials — New Ways to Shrink](https://ir.appliedmaterials.com/static-files/f2fbf48d-a761-4c4f-ab4c-73f312ab1ebc)\n\n[Published lesson data](https://semiaifoundry.com/ai-atlas/lesson-data/plasma-wet-etch-transfer-31e7a07c6260f656.json) · [Atlas releases and licensing](https://semiaifoundry.com/ai-atlas/licenses.html)\n\n## Continue exploring\n\n### Foundations\n\n-   [Coat, bake, expose and develop](https://semiaifoundry.com/learn/atlas/resist-track-development/)\n-   [A process chamber as a controlled reactor](https://semiaifoundry.com/learn/atlas/process-chamber-control/)\n\n### Related lessons\n\n-   [Nanosheet release and replacement gates](https://semiaifoundry.com/learn/atlas/nanosheet-gate-last-integration/)\n-   [TSV formation, keep-out and backside reveal](https://semiaifoundry.com/learn/atlas/hbm-tsv-reveal/)"
}