Si (Silicon)
Semiconductors and photonic platforms integrated-photonics
63 datasets, together covering 0.031–310 µm. Default: Green 2008: n,k 0.25–1.45 µm.
Compare datasets
Tick datasets in the list to add them to the plot (up to 12).
Datasets
Crystalline silicon
- Aspnes and Studna 1983: n,k 0.21–0.83 µm
- Wang et al. 2025: n,k 0.400–1.31 µm; 25 °C
- Wang et al. 2025: n,k 0.400–1.31 µm; 35 °C
- Wang et al. 2025: n,k 0.400–1.31 µm; 45 °C
- Wang et al. 2025: n,k 0.400–1.31 µm; 55 °C
- Wang et al. 2025: n,k 0.400–1.31 µm; 65 °C
- Franta et al. 2017: n,k 0.0310–310 µm; 10 K
- Franta et al. 2017: n,k 0.0310–310 µm; 50 K
- Franta et al. 2017: n,k 0.0310–310 µm; 100 K
- Franta et al. 2017: n,k 0.0310–310 µm; 150 K
- Franta et al. 2017: n,k 0.0310–310 µm; 200 K
- Franta et al. 2017: n,k 0.0310–310 µm; 250 K
- Franta et al. 2017: n,k 0.0310–310 µm; 20 °C
- Franta et al. 2017: n,k 0.0310–310 µm; 25 °C
- Franta et al. 2017: n,k 0.0310–310 µm; 300 K
- Franta et al. 2017: n,k 0.0310–310 µm; 350 K
- Franta et al. 2017: n,k 0.0310–310 µm; 400 K
- Franta et al. 2017: n,k 0.0310–310 µm; 450 K
- Franta et al. 2017: n,k 0.0310–310 µm; 500 K
- Shkondin et al. 2017: n,k 2–20 µm
- Schinke et al. 2015: n,k 0.25–1.45 µm
- Green 2008: n,k 0.25–1.45 µm
- Chandler-Horowitz and Amirtharaj 2005: n 2.5–22.2 µm, k 6.25–23.3 µm
- Šik et al. 1998: n,k 0.29–0.62 µm; 25 °C
- Šik et al. 1998: n,k 0.29–0.62 µm; 250 °C
- Šik et al. 1998: n,k 0.29–0.62 µm; 500 °C
- Šik et al. 1998: n,k 0.29–0.62 µm; 700 °C
- Šik et al. 1998: n,k 0.29–0.62 µm; 850 °C
- Green and Keevers 1995: n 0.25–1.45 µm, k 0.25–1.00 µm
- Daub 1995: k 0.94–1.42 µm
- Vuye et al. 1993: n,k 0.26–0.83 µm; 20 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 100 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 150 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 200 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 250 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 300 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 350 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 400 °C
- Vuye et al. 1993: n,k 0.26–0.83 µm; 450 °C
- Jellison 1992: n,k 0.234–0.840 µm
- Edwards and Ochoa 1980: n 2.5–25 µm
- Li 1980: n 1.2–14 µm; 100 K
- Li 1980: n 1.2–14 µm; 150 K
- Li 1980: n 1.2–14 µm; 200 K
- Li 1980: n 1.2–14 µm; 250 K
- Li 1980: n 1.2–14 µm; 293 K
- Li 1980: n 1.2–14 µm; 350 K
- Li 1980: n 1.2–14 µm; 400 K
- Li 1980: n 1.2–14 µm; 450 K
- Li 1980: n 1.2–14 µm; 500 K
- Li 1980: n 1.2–14 µm; 550 K
- Li 1980: n 1.2–14 µm; 600 K
- Li 1980: n 1.2–14 µm; 650 K
- Li 1980: n 1.2–14 µm; 700 K
- Li 1980: n 1.2–14 µm; 750 K
- Salzberg and Villa 1957: n 1.36–11 µm
Amorphous silicon (a-Si)
- Pierce and Spicer 1972: n,k 0.0103–2.07 µm
- Karaman et al. 2025: n,k 0.21–2.5 µm; 20 °C
- Karaman et al. 2025: n,k 0.21–2.5 µm; 70 °C
- Karaman et al. 2025: n,k 0.21–2.5 µm; 120 °C
- Karaman et al. 2025: n,k 0.21–2.5 µm; 170 °C
- Karaman et al. 2025: n,k 0.21–2.5 µm; 220 °C
- Franta et al. 2013: n,k 0.138–26.9 µm
Use it in Lightsmith
Load this data in Python, simulate in one more line
pip install lightsmith
import lightsmith as ls
# Si (Silicon): Green 2008: n,k 0.25–1.45 µm
si = ls.LibraryMaterial(name="Si", dataset="Green-2008")
print(si.range_nm) # (250, 1450) nm
eps = si.eps([633]) # complex permittivityShips with the library: works offline and is the library's own default dataset choice.
import lightsmith as ls
# Si (Silicon): Green 2008: n,k 0.25–1.45 µm
si = ls.fetch_refractiveindex("main", "Si", "Green-2008", ref="c5c2f188e848453def5970e347399d653df2ffc2")
print(si.range_nm) # (250, 1450) nm
eps = si.eps([633]) # complex permittivityDownloads the original record once (pinned to the imported snapshot) and caches it.
import json, urllib.request
import lightsmith as ls
url = "https://materials.lightsmith.dev/v1/datasets/ds_aa67cc1ecd1b/lightsmith"
si = ls.Tabulated.model_validate(json.load(urllib.request.urlopen(url)))
print(si.source) # citation and provenance travel with the dataAny dataset here, as the library's Tabulated material (si/green-2008).
# ... load si as above, then:
sim = ls.Simulation(
particles=[ls.Particle(shape=ls.Sphere(radius_nm=50), material=si)],
medium_index=1.0,
wavelengths=ls.WavelengthRange(start_nm=400, stop_nm=1000, num=81),
)
res = ls.solve(sim) # exact Mie for a sphere
print(res.q_ext) # extinction efficiency at each wavelengthSame spec runs with DDA, FDTD, BEM or T-matrix: ls.compare(sim, ["mie", "dda"]).
Lightsmith is open source (MIT): Mie, T-matrix, BEM, DDA, FDTD, RCWA, TMM, mode solvers and inverse design behind one spec, on CPU or GPU. GitHub.
About Si (Silicon)
Silicon (Si) is a crystalline, brittle element with a bluish-grey metallic luster. It stands as the second most abundant element in the Earth's crust, primarily in the form of silicates and oxides. In its pure form, silicon is employed extensively in the electronics industry for the fabrication of semiconductors, which form the basis of most modern electronic devices. The ability of silicon to act as a substrate for microelectronic devices stems from its semiconductor properties and the potential to precisely dope it with other elements to modify its electrical characteristics. Furthermore, silicon finds use in the photovoltaic industry in solar cells. When it comes to optics, silicon is transparent to infrared light, making it valuable for infrared lenses and other optical components. However, it's opaque to visible light. The material's versatility and abundance have made it integral to many industries, from construction to electronics and beyond
Other names: Silicon, Polycrystalline silicon, Poly, Semicrystalline silicon, silicon, c-Si, SOI
Also listed in
API: /v1/materials/mat_65a8cefde783 · slug si