Thin Oxides The new frontier

Volume 43, No Special Issue on Ultrathin Oxides

Material issues in ultrathin gate oxides: Surface preparation Boron penetration Trade off of Boron penetration (reduced by adding N) Vs threshold shifts (increasing with nitrogen) Tailoring of N in oxide Trade off of gate depletion (decreasing with increasing B) Vs penetration

Thermodynamically, Nitrogen is not stable in SiO 2, if the partial pressure of oxygen is >1E18 atm (true under all processing conditions). Perhaps N is trapped at interface defects, or is stable because it releases stress at the silicon/silicon dioxide interface It is not clear why nitrogen can be added to the oxide.

Note that the scale for the anneal is in seconds. Boron penetration depends on if the B is implanted as BF 3 or in some other form (related to grain boundary diffusion in the poly gate) Note the drop in yield to 50% as the B penetrates.

Gate oxides grown on previously oxidized silicon on which the oxide was removed. Therefore, your starting surface is the etched, ex-SiO 2 /Si interface. Cleaning is crucial. A long water rinse (contains oxygen gas to various degrees) after buffered HF will re-oxidize this surface. The atomic reconstruction of the Si surface prior to oxidation also may play a role (includes ramp up). Initial oxide growth rate in the presence of a 10 eV electron probe beam. Note inverse relation to temperature. The sticking coefficient of oxygen decreases with increasing temperature.

Standard RCA clean: SPM: H 2 SO 4 /H 2 O 2 (Chemical oxide growth, organics removal Dilute HF (Particle, Metal removal, H stabilized surface) SC1: NH 4 OH/H 2 O 2 /H 2 O (Clean Chemical oxide growth) SC2: Metal removal HCl/H 2 O 2 /H 2 O (Metal removal) Dilute HF dip

pH = 3pH = 1 Isoelectric point of Si and SiO 2 is pH ~ 2 At low pH surface is positively charged (“H + pressure” ) At higher pH surface is negatively charged Most particles are negatively charged A. ISOELECTRIC POINT OF SIO 2 good Not so good

More dilute acids deter particle settling on the wafer but are less effective in destroying organic or in driving chemical oxide growth. Buffered HF has a pH of 8-9. It is not like HF This diagram is for pure SiO 2. The potential of real wafers (Si, SiN x etc) will be different.

B. POURBAIX DIAGRAM If the wafer is > 0.5 V negatively charged, Fe will plate out.

The plating of metals, especially those like Au, Ag, Cu out of HF is a problem. Even though the concentration of these impurities is low (< 1 ppb), large quantities of HF flow over the wafer surface and a very low density of metals (1/000 of one monolayer) can kill all MOS capacitors. Present on the wafer, the metals will roughen the Si during ramp up. Since the reduction (plating) of metal ions involves charge transfer, it occurs slower if the wafers are rinsed in the dark. Minority carrier Surface states C. PLATINGS OF METALS

Minority carrier Heyns et al, IBM Trace HCl (often present involuntary in HF) reduce plating in light exposed wafers. Note that the concentration of copper can reach densities comparable to the density of Si atoms at the surface. The reduction with trace amounts of HCl (ppm !!!) is due to catalytic action

D. PITFALLS OF STATISTICS The charge to breakdown is a function of area. This is simply a result of statistics. Large areas are more likely to contain a defect. Depending on area used to test, N 2 O either improves or worsens the oxide !!

Addendum: Weibull distribution The Weibull distribution comes in a 2 parameter and 3 parameter form. The 2 parameter form is called the standard Weibull distribution. The two parameters are  and .  is know as the slope and  is known as the scale parameter. There is now deep physical meaning to the Weibull distribution. It is a probability density function that happens to fit a large number of physical phenomena, from the distribution of wind speeds to the breakage strength of glass fibers to the failure rate of electronic devices. One reason is that the function can look quite different for different parameters.

The parameters are found by plotting the data on a special Weibull graph paper. Assume that 6 identical capacitors are tested, and that they fail after 93, 34, 16, 120, 53 and 75 hours.

The average time to failure is 65 hours. Based on this we would rank the devices, in order of increasing lifetime 116hrs24% 234hrs52.% 353hrs81% 475hrs115% 593hrs143% 6120hrs184% This is cute but not very useful, since we know that it is likely that if we keep testing we will likely find a device which last longer, say 180 hrs. We would rather calibrate to this. If we do this correctly we using Exact Medium Ranks *, MR, we get 116hrs10.9% 224hrs26.4% 353hrs42.1% 475hrs57.9% 593hrs 73.6% 6120hrs89.6% * An approximate formula is where i is the order, and N the number of samples tested

Now plot the data. By matching the slope we determine  equals 1.4 and  corresponds to 76 hours. This plots says that the probability of a capacitor to fail after 10 hrs is 5%. The probability of a capacitor to fail after 200 hrs is 99%.