High-speed digital light source photocurrent mapping system

被引:2
|
作者
Bausi, Francesco [1 ]
Koutsourakis, George [1 ]
Blakesley, James C. [1 ]
Castro, Fernando A. [1 ]
机构
[1] Natl Phys Lab, Teddington TW11 0LW, Middx, England
基金
欧盟地平线“2020”;
关键词
photovoltaics; photodetectors; current measurement; characterization; LBIC; BEAM-INDUCED CURRENT; DEFECTS; CELLS;
D O I
10.1088/1361-6501/ab1f40
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
High-resolution spatial characterization of photovoltaic devices and photodetectors can reveal local defects that can have a detrimental impact on the lifetime and performance of such devices. Photocurrent mapping methods can provide high-resolution measurements and characterization can be achieved under actual operating conditions. However, such methods usually require costly, complicated systems and possess limited measurement speed. In this work an optical system based on a digital micro-mirror device is used for photocurrent mapping and a measurement protocol with a theoretical upper limit for scanning rate of 22 kHz is presented. The digital micromirror device itself provides synchronised spatial and temporal modulation in order to perform current mapping. Photocurrent maps of solar cells devices obtained with a scanning rate of 1000 pixels in less than 6 s are reported (the speed was limited by the device's response time and its photon to current conversion efficiency). The speed of photocurrent mapping is thus increased by almost two orders of magnitude compared to other methods and is only limited by the response of the device under test. A lateral resolution of 34 mu m is achieved, with the potential to increase it even further. The absence of any moving parts allows high repeatability of measurements. Combined with the high-speed control of the light field, this enables the development of novel measurement techniques for the simultaneous measurement of temporal and spatial parameters. The fully digital control of the mapping system also presents a high potential for integration in industrial automated systems as it can be fully controlled using machine learning algorithms to achieve fast detection of crucial defects and features of devices during manufacturing.
引用
收藏
页数:9
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