Wafer imaging and processing method and apparatus
A method is disclosed whereby luminescence images are captured from as-cut or partially processed bandgap materials such as multicrystalline silicon wafers. These images are then processed to provide information is then utilized to predict various key parameters of a solar cell manufactured from the bandgap material, such as open circuit voltage and short circuit current. The information may also be utilized to apply a classification to the bandgap material. The methods can also be used to adjust or assess the effect of additional processing steps, such as annealing, intended to reduce the density of defects in the bandgap materials.
1. A method of classifying a plurality of silicon wafers for photovoltaic cell manufacture, said method comprising the steps of:
(a) capturing a photoluminescence image of a whole silicon wafer of each of said plurality of silicon wafers for photovoltaic cell manufacture;
(b) processing each of said images to obtain information about defects in each of said plurality of silicon wafers;
(c) utilizing said information about defects obtained from said photoluminescence image of a whole silicon wafer to apply a classification to said silicon wafer as a whole for each of said plurality of silicon wafers; and
(d) using said classification to bin each of said silicon wafers into different quality categories based on predicted operational characteristics of photovoltaic cells manufactured from said silicon wafers,
wherein said processing step obtains information that includes the area sum or area average of dislocation densities in each of said plurality of silicon wafers.
2. The method according to claim 1 , further comprising the step of performing the method on one or more wafers cut from adjacent or nearby slices of an ingot or block of silicon, and interpolating or extrapolating the results to predict the operational characteristics of photovoltaic cells manufactured from one or more neighboring wafers cut from said ingot or block, or to apply a classification to said neighboring wafers.
3. The method according to claim 1 , further comprising the step of locating low material quality regions caused by edge defects or impurities along an edge of a silicon wafer.
4. The method according to claim 1 , further comprising the step of utilizing the classification or predicted operational characteristics to alter parameters associated with a series of processing steps in the formation of a photovoltaic cell so as to improve the quality of said photovoltaic cell.
5. The method according to claim 4 , wherein said parameters include the conditions for firing a metal pattern into a silicon wafer.
6. The method according to claim 4 , wherein said parameters include the diffusion conditions for diffusing materials into a silicon wafer, or parameters for any other process that creates doped regions in a silicon wafer.
7. The method according to claim 1 , further comprising the step of normalizing each said photoluminescence image with regard to the background doping level of each of said silicon wafers.
8. The method according to claim 1 , wherein said area sum or area average of dislocation densities is weighted for the location of said dislocations relative to the grid lines or other metallic contacts of a photovoltaic cell to be manufactured from a silicon wafer.
9. The method according to claim 1 , wherein said area sum or area average of dislocation densities is weighted according to the severity of said dislocations.
10. The method according to claim 9 , wherein said severity is assessed based on relative intensity variations in a photoluminescence image.
11. The method according to claim 1 , further comprising the step of obtaining information about dislocation defects in each of said plurality of silicon wafers by using microwave photoconductance decay, optical transmission, or optical reflection measurements.
12. The method according to claim 11 , wherein said optical transmission or optical reflection measurements are performed in the 1400 nm-1700 nm spectral range.
13. The method according to claim 1 , wherein said operational characteristics include open circuit voltage, short circuit current density, fill factor or efficiency.
14. The method according to claim 1 , wherein the classification of said plurality of silicon wafers includes rejecting or pricing of said silicon wafers.
15. A system for classifying a plurality of silicon wafers for photovoltaic cell manufacture, said system comprising:
a photoluminescence capture system for generating and capturing a photoluminescence image of a whole silicon wafer of each of said plurality of silicon wafers;
an image processor for processing each said photoluminescence image to obtain information about defects in each of said plurality of silicon wafers; and
a classifier for utilizing said information about defects obtained from said photoluminescence image of a whole silicon wafer to apply a classification to said silicon wafer as a whole for each of said plurality of silicon wafers and for using said classification to bin each of said silicon wafers into different quality categories based on predicted operational characteristics of photovoltaic cells manufactured from said silicon wafers,
wherein the image processor obtains information that includes the area sum or area average of dislocation densities in each of said plurality of silicon wafers.
16. The method according to claim 1 , wherein said processing step obtains information that includes the relative distribution of dislocations in each of said plurality of silicon wafers.
17. The method according to claim 16 , wherein said relative distribution of dislocations is weighted for the location of said dislocations relative to the grid lines or other metallic contacts of a photovoltaic cell to be manufactured from a silicon wafer.
18. The method according to claim 16 , wherein said relative distribution of dislocations is weighted according to the severity of said dislocations.
19. The method according to claim 18 , wherein said severity is assessed based on relative intensity variations in each said photoluminescence image.
20. The method according to claim 1 , further comprising the step of using the wafer binning to sort said plurality of silicon wafers for photovoltaic cell manufacture.