Integrating Industrial Black Silicon with High-Efficiency Solar Cells-Yu Zhang

Faculty of Engineering School of Photovoltaic and Renewable Energy Engineering Integrating Industrial Black Silicon with High-Efficiency Solar Cells Yu Zhang1, Malcolm D. Abbott1,2, Bram Hoex1, David N. R. Payne1,3, Tsun H. Fung1, Muhammad U. Khan1, Shaozhou Wang1, Anastasia Soeriyadi1, Bruno V. Stefani1, Rasmus S. Davidsen4, Maksym Plakhotnyuk4, Ole Hansen4, Darren Bagnall3 and Keith R. McIntosh2 1 University of New South Wales, Sydney, NSW, Australia 2 PV Lighthouse, Coledale, NSW, Australia 3 Macquarie University, Sydney, NSW, Australia 4 Technical University of Denmark (DTU), Denmark 2 Project scope 2. Develop rapid modelling techniques for bSi 4. Improve industrial integration of bSi with high efficiency processing: a) Diffusion, b) Passivation, c) Hydrogenation, d) Laser doping, e) Contact formation. Ultimately leading to bSi Solar Cells with improved efficiency 1. Study fundamental properties of black silicon (bSi) 3. Establish improved characterisation techniques - For fundamentals studies - Industrially relevant techniques for in-line monitoring 3 UNSW Collaboration – We’re not alone Canadian Solar Primary bSi wafer supply Project funder UNSW Characterisation and analysis of textures – Test structure and cell fabrication - Rapid & Scalable optical modelling development - Texture and subsequent process interaction analysis - BSi LeTID determination and mitigation 1366 Technologies bSi wafer supply Macquarie University Optical characterisation & Modelling DTU Technical University of Denmark RIE bSi wafer supply AMOLF Finite element optical modelling Oxford & Southampton University Novel textures & high efficiency device integration ARENA Australia Renewable Energy Agency Project funder Input Outp ut Canadian Solar Improved understanding of BSi potential and limitations - Improved process integration - Enhanced mitigation of BSi LeTID ARENA Milestone reporting & knowledge sharing Academic Partners Joint publications 1366 Technologies Improved understanding of best texture for unique direct wafer substrates 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 0 10 20 30 40 50 60 70 80 90 100 To tal Re flecta nce (%) Wav elen gth (n m)Black Silicon UNSW Database 4 Optical Topography Reflectance/ Absorptance/ Transmittance SEM LBIC Uniformity AFM • Data gathered for a broad range of bSi to help us understand different surface features, including: How do we characterise the topography? • Scanning electron microscopes (SEM) for imaging surfaces • Atomic force microscopes (AFM) used for quantifying topography • To investigate larger scale roughness (or ‘waviness’) a 3D laser scanning microscope is used 5 How do we characterise the topography? • Scanning electron microscopes (SEM) for imaging surfaces • Atomic force microscopes (AFM) used for quantifying topography • To investigate larger scale roughness (or ‘waviness’) a 3D laser scanning microscope is used 6 500nm Characterization Techniques - Topography Challenges • AFM is useful for mapping nanoscale surfaces in 3D, but it has some limitations and can give erroneous results for high aspect-ratio features 7 DTU 16min Surface Area (um2) DDESP 123.53 HAR1-200A 305.57 OTESPA-R3 279.20 RTESPA-150 318.73 TESPD 290.84 Characterization Techniques - Topography Challenges • AFM is useful for mapping nanoscale surfaces in 3D, but it has some limitations and can give erroneous results for high aspect-ratio features 8 Characterization Techniques - Topography Challenges 9 • We are implementing in-house advanced characterisation based on focussed ion beam (FIB) tomography • This will enable 3D mapping of a nano-surface without limitations on feature shape or aspect ratio Automatically extract cross sections slice by slice Characterization Techniques - Topography Challenges 10 • We are implementing in-house advanced characterisation based on focussed ion beam (FIB) tomography • This will enable 3D mapping of a nano-surface without limitations on feature shape or aspect ratio How do we characterise the optical? • Spectrophotometer used to measure average reflectance (R), transmittance (T), absorption (A) and Haze (H) over a broad wavelength range • Total and diffuse (scattered) components of R and T are separated using the integrating sphere specular exclusion port technique 11 http://olympus.magnet.fsu.edu/primer/java/reflection/specular/index.html 𝐻𝑎𝑧𝑒𝑅 % = 𝑅𝑠𝑐𝑎𝑡𝑡𝑒𝑟𝑅 𝑡𝑜𝑡𝑎𝑙 ×100% Characterization Techniques – Optical Uniformity • For MCCE, the background waviness is always grain-dependent. • Uniformity across a wafer is investigated by laser beam induced current (LBIC) Scanner, at several discrete wavelengths. 12 Characterization Techniques – Optical Uniformity 13 • Two MCCE recipes with different uniformity. Proposed Advanced Characterisation - Optical • For more comprehensive optical characterisation, UNSW is nearing completion of our unique Scatterlite tool. • This tool uses a high power collimated white light source and gives full control of the sample and detector angles 14 Proposed Advanced Characterisation - Optical • This tool is capable of measuring: 15 - Effects of polarisation - R and T at any angle of incidence - The wavelength and angular distribution of scattered light (at any angle of incidence) - Uniformity mapping of any of the above, down to the micron scale - May identify texture ‘fingerprints’ that can be used for in-line monitoring & quality control. Example scattering profile measured for a textured TCO Why Do We Measure Haze? – Modelling Challenge EM Regime Mixed Regime GO Regime • Features λ of light can be easily understood and modelled using ray tracing under geometric optical theory (GO) • Features λ of light can approximated for rapid modelling using effective medium under electromagnetic theory (EM) • Features sizes in-between are more complicated. 16 The surface texturing turns into an ARC layer! 0.01 0.1 1 10 0.01 0.1 1 10 σ/ τ σcos(ϴ0)/λ EM GO Specular K. Tang, R.A. Dimenna, R.O. Buckius, Regions of validity of the geometric optics approximation for angular scattering from very rough surfaces, Int. J. Heat Mass Transf. 1 (1996) 49–59. 17 Validity Chart σ τ ϴ0 λ Root Mean Square Correlation Length Incoming light angle light wavelength K. Tang, R.A. Dimenna, R.O. Buckius, Regions of validity of the geometric optics approximation for angular scattering from very rough surfaces, Int. J. Heat Mass Transf. 1 (1996) 49–59. 0.01 0.1 1 10 0.01 0.1 1 10 σ/ τ σcos(ϴ0)/λ Specular approximation region Electromagnetic (EM) theory region Geometric optics (GO) approximation region 18 Validity Chart σ τ ϴ0 λ Root Mean Square Correlation Length Incoming light angle light wavelength K. Tang, R.A. Dimenna, R.O. Buckius, Regions of validity of the geometric optics approximation for angular scattering from very rough surfaces, Int. J. Heat Mass Transf. 1 (1996) 49–59. 0.01 0.1 1 10 0.01 0.1 1 10 σ/ τ σcos(ϴ0)/λ Random Pyramids Iso-texturingSpecular approximation region Electromagnetic (EM) theory region Geometric optics (GO) approximation region 19 Validity Chart σ τ ϴ0 λ Root Mean Square Correlation Length Incoming light angle light wavelength K. Tang, R.A. Dimenna, R.O. Buckius, Regions of validity of the geometric optics approximation for angular scattering from very rough surfaces, Int. J. Heat Mass Transf. 1 (1996) 49–59. 0.01 0.1 1 10 0.01 0.1 1 10 σ/ τ σcos(ϴ0)/λ Random Pyramids Iso-texturing RIE 4min RIE 16min RIE 14kW RIE 18kW Specular approximation region Electromagnetic (EM) theory region Geometric optics (GO) approximation region 0.01 0.1 1 10 0.01 0.1 1 10 σ/ τ σcos(ϴ0)/λ Random PyramidsIso-texturing RIE 4min RIE 16min RIE 14kW RIE 18kW MCCE SP1 MCCE SP2 MCCE SP3 MCCE SP4 MCCE SP5 MCCE SP6 20 Validity Chart Specular approximation region Electromagnetic (EM) theory region Geometric optics (GO) approximation region σ τ ϴ0 λ Root Mean Square Correlation Length Incoming light angle light wavelength K. Tang, R.A. Dimenna, R.O. Buckius, Regions of validity of the geometric optics approximation for angular scattering from very rough surfaces, Int. J. Heat Mass Transf. 1 (1996) 49–59. Characterisation and Modelling Challenges 30 40 50 60 70 80 90 100 250 450 650 850 1050 Re flec tance H aze ( %) Wavelength (nm) 21 0 10 20 30 40 50 300 500 700 900 1100 1300Re flec tio n (% ) Wavelength (nm) GO EM Ref (sim) Ref (sim) - LT Ref (meas) 𝑹𝒕𝒐𝒕𝒂𝒍 𝝀 = 𝑯𝒂𝒛𝒆𝑹 𝝀 ∙𝑹𝑮𝑶 𝝀 +(𝟏−𝑯𝒂𝒛𝒆𝑹(𝝀))∙𝑹𝑬𝑴 𝝀 Implication of Reflectance Haze 22 D. Payne, et al. “Understanding the Optics of Industrial Black Silicon”, Silicon PV 2018 0 10 20 30 40 50 60 70 80 250 450 650 850 1050 1250 Re flec tance (% ) Wavelength (nm) R1 R2 R3 M1 M2 M3 iso 30 40 50 60 70 80 90 100 250 500 750 1000 1250 Re flec tance Ha ze (% ) Wavelength (nm) R1 R2 R3 M1 M2 M3 iso • Current state-of-art MCCE wafers tend to have 100% Haze over the spectrum. • RIE is more likely to have Haze100%, due to its extreme feature size. • By combining GO and EM using Haze as a mixing ratio, we are now able to model most bSi reflection curves accurately. Overview 23 • UNSW has comprehensive characterisation capability for black silicon. • UNSW now has a black silicon database, which we continue to build. • Many forms of black silicon cannot be accurately modelled by ray tracing. • Haze can be used as a mixing ratio to combine GO and EM, enabling rapid and accurate modelling. All forms of collaboration are welcome! Please do not hesitate to contact: Dr. Malcolm Abbott: m.abbott@unsw.edu.au Dr. David Payne: d.n.payne@unsw.edu.au Acknowledgements The authors would like to thanks all institutions and industry partners involved in this collaboration. This work was supported in part by the Australian Government through the Australian Renewable Energy Agency (ARENA) 2017/RND009 project. The responsibility for the views, information, or advice expressed herein is not accepted by the Australian Government. 24