In order to profile dopant concentrations in silicon using the alternating current STM, we use a difference frequency mixing strategy. In addition to the conventional DC bias applied to the tunnel junction, two frequencies are introduced from tunable waveform generators. The frequencies are offset by a small amount (typically 5 kHz) that becomes the detected frequency (f1 - f2 = Df). The mixing product (Df) is created due to the nonlinear nature of the tunnel junction and sample. It is selected to be low frequency, so it is conveniently extracted and detected using a lock-in amplifier. This detection strategy allows us to tune the instrument to fundamental frequencies from 0-20 GHz, while the detected output signal detected remains at a constant frequency.
To map out the frequency and voltage response as a function of dopant type and concentration, cleaned doped Si substrates were used. The difference frequency signal was plotted as a function of applied frequency and voltage, for both p- and n-type Si. The results can be seen in Figure 1, which shows that the difference frequency signal is strongly dependent on the fundamental frequency. Figure 1a shows data for highly doped p-type Si (0.001 W-cm boron-doped). A wide peak is centered at -0.7 V sample bias. Figure 1b shows data for lightly doped n-type Si (1-3 W-cm phosphorous-doped). The peak for phosphorous occurs close to 0 V bias. There is a clear difference between the peak location for boron-doped vs. phosphorous-doped Si, demonstrating our ability to distinguish between dopant types. A large nonlinear effect is seen even at low frequencies providing the difference frequency signal necessary for semiconductor characterization.

The magnitude of the difference frequency signal has been measured as
a function of dopant concentration. The difference frequency signal
dependence on dopant density can be seen in Figures 2c and 2d. The
simultaneously acquired I-V curves are seen in Figures 2a and 2b.
The low resistivity, 1-3 W-cm sample, with high
dopant concentration, produced a large peak near 0 V sample bias.
The high resistivity, 20 W-cm sample, with low
dopant concentration, produced almost an order of magnitude less signal.
Similar results were observed for varying p-type dopant concentrations.
From these and related results, we ascertain that the magnitude of the
difference frequency signal depends strongly and monotonically on the dopant
density. The results also demonstrate the capability of the ACSTM
to distinguish between areas of high and low concentration using difference
frequency detection. This capability is important for imaging patterned
substrates.

After confirming our ability to differentiate between different dopant levels and types, we imaged a patterned surface with ~1 mm x 2 mm p+ pads on n-type Si. The interface between the pad and surface is shown in Figure 3. The difference frequency map was recorded with applied modulation signals at 10.000000 MHz and 10.005000 MHz, with a DC sample bias of +1.5 V. The upper left region of the image is n-type Si, while the lower right area of the image is p-type Si. This assignment can also be confirmed spectroscopically.

If you have any questions regarding this ongoing work, please contact me: zjd101@psu.edu.