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  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 6, Iss. 8 — Aug. 26, 2011
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Cluster LEDs mixing optimization by lens design techniques

Ming-Chin Chien and Chung-Hao Tien  »View Author Affiliations


Optics Express, Vol. 19, Issue S4, pp. A804-A817 (2011)
http://dx.doi.org/10.1364/OE.19.00A804


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Abstract

This paper presents a methodology analogous to a general lens design rule to optimize step-by-step the spectral power distribution of a white-light LED cluster with the highest possible color rendering and efficiency in a defined range of color temperatures. By examining a platform composed of four single-color LEDs and a phosphor-converted cool-white (CW) LED, we successfully validate the proposed algorithm and suggest the optimal operation range (correlated color temperature = 2600–8500 K) accompanied by a high color quality scale (CQS > 80 points) as well as high luminous efficiency (97% of cluster’s theoretical maximum value).

© 2011 OSA

1. Introduction

Light-emitting diode (LED) technology has profoundly changed the way light is generated across a wide field of applications due to its unique characteristics, including possibly the highest optoelectronic conversion efficiency as well as the capability of modulating spectral composition and environmentally benign raw materials [1

1. J. K. Kim and E. F. Schubert, “Transcending the replacement paradigm of solid-state lighting,” Opt. Express 16(26), 21835–21842 (2008), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-16-26-21835. [CrossRef] [PubMed]

]. Among these features, one challenge in the design of a LED-based cluster is how to adjust the spectral power distribution (SPD) in an underdetermined condition, thus enabling us to manipulate strategically the chromaticity point, light quality, and system efficiency according to different operational purposes. For example, we are able to enhance the fidelity appearance in high-color-quality mode or to employ higher efficiency at a sacrifice of color rendering in an unoccupied area [2

2. E. F. Schubert and J. K. Kim, “Solid-state light sources getting smart,” Science 308(5726), 1274–1278 (2005). [CrossRef] [PubMed]

].

The mixing question for a white LED cluster can be separated into three aspects:

  • (a) Energy––the most widespread figures of merit from the viewpoint of energy are the luminous efficacy of radiance (LER) and the luminous efficiency (LE). The LER represents the amount of luminous flux (lumen) converted from a per-unit optical power (watt), whereas the LE is defined as the luminous flux normalized to the electrical input power (watt) expended to operate the LED. In principle, the LE is the product of the LER and electric-to-optical power conversion efficiency [3

    3. E. F. Schubert, Light-emitting Diodes, 2nd ed. (Cambridge University Press, 2006).

    ]. In order to approach the relationship in terms of efficiency and color rendering, A. Žukauskas et al. found an optimal boundary (Pareto front) to address the fundamental tradeoff between the LER and the color rendering index (CRI) via an LED-primary-based approach [4

    4. A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, R. Gaska, and M. S. Shur, “Optimization of white polychromatic semiconductor lamps,” Appl. Phys. Lett. 80(2), 234–236 (2002). [CrossRef]

    ]. The optimal boundary subject for one artificial SPD has the potential to provide a useful guide in the design of a polychromatic system. To date, G. He et al. adopted a more practical index, LE, as a merit figure and transferred this concept into laboratory practice, where different LED white composite spectra were analyzed and realized over a range of color temperatures [5

    5. G. He and L. Zheng, “Color temperature tunable white-light light-emitting diode clusters with high color rendering index,” Appl. Opt. 49(24), 4670–4676 (2010). [CrossRef] [PubMed]

    ,6

    6. G. He and L. Zheng, “White-light LED clusters with high color rendering,” Opt. Lett. 35(17), 2955–2957 (2010). [CrossRef] [PubMed]

    ].
  • (b) Light quality––the major characteristic of white light quality is its ability to reproduce colors of illuminated objects with high fidelity, i.e., as close as possible to those perceived under sunlight or blackbody radiators. The CRI proposed by the CIE (Commision Internationale de l’Éclairage) is the most widely recognized figure of merit. However, CRI has been criticized for its lack of fidelity in ranking sources, especially those with highly peaked spectra such as LEDs [7

    7. Y. Ohno, “Color rendering and luminous efficacy of white LED spectra,” Proc. SPIE 5530, 88–98 (2004). [CrossRef]

    ]. One of the major deficiencies is the penalization of sources that produce high-chromatic saturation, which is actually preferred for human vision. As a consequence, numerous refinements are being explored, such as the color quality scale (CQS) [8

    8. W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010). [CrossRef]

    ], gamut area index (GAI) [9

    9. M. S. Rea and J. P. Freyssinier-Nova, “Color rendering: a tale of two metrics,” Color Res. Appl. 33(3), 192–202 (2008). [CrossRef]

    ], and color saturation index (CSI) [10

    10. A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, H. Vaitkevičius, P. Vitta, and M. S. Shur, “Statistical approach to color quality of solid-state lamps,” IEEE J. Sel. Top. Quantum Electron. 15(6), 1753–1762 (2009). [CrossRef]

    ].
  • (c) Mixing scheme––the SPD of an LED cluster can be synthesized by using (i) additive mixing of two or more single-color LED chips (LED-primary-based approach), (ii) wavelength-conversion via using phosphors or other materials (LED-plus-phosphor-based approach), and (iii) a hybrid approach composed of (i) and (ii) [11

    11. E. F. Schubert, J. K. Kim, H. Luo, and J.-Q. Xi, “Solid-state lighting––a benevolent technology,” Rep. Prog. Phys. 69(12), 3069–3099(2006). [CrossRef]

    ].

The prior SPD optimizations were addressed mainly via multiple single-color LEDs and usually had been restricted to certain specific conditions, such as CRI, LER, and so forth [4

4. A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, R. Gaska, and M. S. Shur, “Optimization of white polychromatic semiconductor lamps,” Appl. Phys. Lett. 80(2), 234–236 (2002). [CrossRef]

]. Although several cases using a hybrid approach have been proposed for color temperature adaptable systems [5

5. G. He and L. Zheng, “Color temperature tunable white-light light-emitting diode clusters with high color rendering index,” Appl. Opt. 49(24), 4670–4676 (2010). [CrossRef] [PubMed]

,6

6. G. He and L. Zheng, “White-light LED clusters with high color rendering,” Opt. Lett. 35(17), 2955–2957 (2010). [CrossRef] [PubMed]

], to our knowledge, there is a lack of general SPD synthesizing rules for practical LED clusters, which can systematically and efficiently optimize SPD for certain user-defined lighting qualities. In this paper, we make an attempt to borrow design techniques from a conventional lens system and offer a solution with wider operation windows to cover aforementioned environments. Our ultimate goal is to develop a general LED design procedure in a more complete treatment. The design flow in all respects can be closely analogous to a conventional lens design process that has long been developed by which the SPD of an LED cluster can be optimized by going through every step of the modeling. All the figures of merit affected by different factors are discussed, along with the experimental validation of an LED cluster that will be examined.

First, we emulate a single-color LED as a singlet, whose light-bending power determined by its curvature and refractive index can be conceptually analogous to the emitting luminous flux of an LED determined by the driving current and LE, respectively. As we mix a number of LEDs, the additive mixing by two single-color LEDs is equivalent to two singlet lenses. Likewise, the LED-plus-phosphor-based approach can be regarded as a cemented doublet (dichromatic) or triplet (trichromatic), depending on the number of emitting peak wavelengths. The concept is schematized in Fig. 1
Fig. 1 Conceptual analogy between the SPD synthesis and conventional lens design. An LED cluster composed of red/cool-white/cool-white/green (R/CW/CW/G) can be regarded as a double Gauss lens system with two singlet lenses and two cemented doublets, where the CW LED is caused by dichromatic mixing.
. Based on the hypothesis outlined above, the SPD synthesis can be transformed into a classic lens design problem. For example, an LED cluster composed of red/cool-white/cool-white/green (R/CW/CW/G) is logically equivalent to a double Gauss lens system. The fundamental constraint such as diffraction limitation of a lens system is viewed accordingly as the theoretical boundary of the LER or CRI.

2. Concept of Design Procedure

The solution of a lens design is a typical inverse problem. Given the effective focal length (EFL) and degree of correction for an optical system, it is always possible to determine the curvatures, thicknesses, and number of lenses in sequence. For example, if we aim to design a lens system with a specified EFL and correct three Seidel aberration coefficients, it can be resolved analytically by a set with two singlet lenses; that leaves four degrees of freedom––two powers and two shape factors [the shape factor is defined as (R2 + R1)/(R2 − R1), where R1 and R2 are the radii of the first and second surfaces, respectively]. Since the complexity of multiple lenses would increase the computational cost, a more efficient method in lens design would resort to an iterative process, as shown in Fig. 2(a)
Fig. 2 Design procedure of (a) lens design and (b) spectral synthesis of a LED cluster. Both flow charts include six steps: (2.1) initial system, (2.2) define boundary condition, (2.3) optimization, (2.4) aberration or merit analysis, (2.5) judgment, and (2.6) tolerance analysis.
.

Similarly, we adopt this idea by replacing the lens set with a number of LEDs for certain predefined environments, as proposed in Fig. 2(b). The design procedure includes six steps: (2.1) initial system, (2.2) define boundary condition, (2.3) optimization, (2.4) aberration or merit analysis, (2.5) judgment, and (2.6) tolerance analysis, and each step is discussed below.

2.1. Initial System

Like a glass map in lens design, LED manufacturers offer a broad range of LED datasheets with available materials and peak wavelengths [12

12. Epistar Corporation, Taiwan, General LED product catalog (2010).

,13

13. Toyoda Gosei Corporation, Japan, LED product catalog (2010).

]. The dependence of LE on peak wavelengths can be analogous conceptually to a refractive index versus an Abbe number. It is known that the lens with the higher refraction index possesses higher bending power. Therefore, green- (505 nm) and amber- (595 nm) color LEDs would serve as appropriate candidates in the consideration of high LE, as shown in Fig. 3
Fig. 3 Normalized LE of visible LED made from GaInN and AlGaInP series versus individual peak wavelength. The LED with high LE is analogous to a lens with high-refractive index.
.

If we plan to mix two single-color LEDs for a specific correlated color temperature, TCC, the most straightforward solution is to select two complementary peak wavelengths on the chromaticity diagram. However, the question becomes more complex when multiple figures of merit are considered by a number of LEDs. To pick an appropriate LED set in a systematic way, we list three suggestions for the initial system [14

14. R. Kingslake, Lens Design Fundamentals (Academic Press, 1978).

]:

  • 1. A mental guess. This way is workable for an expert, while it is laborious for a beginner.
  • 2. A designed case from previous literature. It is the most common way to choose a design close to your requirements.
  • 3. A search through the patent files. This is also time-consuming work, and consideration of avoiding the patent’s claims in your design is necessary.

2.2. Define Boundary Condition

The color mixing for such a condition can be described as in Eq. (1),
T_i_=t_,
(1.a)
T_=[X1X2...Y1Y2...Z1Z2...    Xi...XMYi...YMZi...ZM]i=1, ...,M,  i_=[I1I2IiIM]i=1, ...,M,  and  t_=[XYZ],
(1.b)
where t represents the resulting tristimulus of an unknown input LED vector i (M-element) projected on the reference matrix T [18

18. G. Wyszecki, and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data, and Formulae (Wiley, 2000).

]. If the system is critically determined (M = 3), there exists a single solution (I1, I2, I3) to enable the resulting tristimulus (X, Y, Z) as possibly closest to the target tristimulus (XT, YT, ZT). Because the light response in human vision is relatively insensitive at a high luminance level, we can simply confine chromatic deviation Δxy < 0.01, which commonly is used in the lighting industry:

||(x, y)(xT, yT)||<0.01.
(2)

Once the system is underdetermined (M > 3), Ii has multiple results to satisfy Eq. (2). For such a case, in addition to the chromaticity point the physical limitation for each LED current Ii should be imposed as in Eq. (3),

0< Ii <max Ii,
(3.a)
max Ii=min[(max Ii in datasheet), (max Ii for Yi=YT )].
(3.b)

So far, T is assumed to be a constant reference matrix. However, the entries of the matrix would be a function of input current Ii .To include the dependence of Ii, we then rewrite the column entry (Xi, Yi, Zi) in Eq. (4) as

Xi=380780x˜(λ)SPDi(λ, λ0,i, Δλi)dλ,
(4.a)
Yi=380780y˜(λ)SPDi(λ, λ0,i, Δλi)dλ,
(4.b)
Zi=380780z˜(λ)SPDi(λ, λ0,i, Δλi)dλ.
(4.c)

Reference [5

5. G. He and L. Zheng, “Color temperature tunable white-light light-emitting diode clusters with high color rendering index,” Appl. Opt. 49(24), 4670–4676 (2010). [CrossRef] [PubMed]

] shows the dependence of SPDi(λ, λ0 , i, Δλi) on input current Ii under a constant ambient temperature (Ta). In order to fully characterize SPDi(λ, λ0 , i, Δλi), we generalize the model that includes the influences of ambient temperature on λ0 , i and Δλi (Fig. 4
Fig. 4 Emission spectrum of a phosphor-converted CW LED under different Ta. The blue and fluorescence spectrum have individual temperature dependence of λ0 . i and Δλi that should be considered in SPD modeling of this kind of LED.
). Accordingly, λ0 , i and Δλi, respectively, are no longer constants but functions of (Ii, Ta).

Both λ0 , i(Ii, Ta) and Δλi(Ii, Ta) are a typical “two-input single-output” system, also called a surface fitting (SF) model [19

19. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing (Prentice Hall, 1997).

]. The typical SF model can be decomposed by the basic functions fj(μ),
ν=j=1najfj(μ_),
(5)
where μ is a vector with two inputs, e.g., Ii and Ta, and ν is the corresponding output, e.g., λ0 , i or Δλi. The term aj is the jth unknown coefficient. With appropriate basic functions, a set of sample data (μ k, νk) k = 1··· m by experimental results can be imported into Eq. (5) and expressed in a matrix form:

F_a_=v_,
(6.a)
F_=[f11f12......fm1fm2...    f1j...f1n...fmk...fmn]k=1, ...,mj=1, ...,n,   a_=[a1a2ajan]j=1, ...,n,  and   v_=[ν1ν2νkνm]k=1, ...,m.
(6.b)

Equation (6) is generally over-determined, m >> n, so that the coefficient vector a should be solved by minimizing the error function E(a) defined in Eq. (7) as

E(a_)=(v_F_a_)T(v_F_a_).
(7)

The SPDi(λ, λ0 , i, Δλi) is then obtained and usually is expressed by the Gaussian function. In addition, compensation terms, λ0 , i(ti), Δλi(ti), and SPDi(ti), could be attached to corresponding factors where device-aging dependence could be included.

2.3. Optimization

For the lens design, it is likely to have identical EFLs due to a combination of different curvatures and thicknesses of the prescribed elements. Therefore, an additional mechanism of assessment, usually adopting a merit function, is necessary for the optimization process. Generally, the merit function of a lens system shall include the aberrations and should evaluate the impact of each parameter change on image quality. Similarly, different SPDs subject to different combinations of driving currents would result in the metamerism. A user-defined merit function is essential in order to consider the dependence of SPD on predefined performance. Equation (8) is an example of where we set the LE and CQS as the figures of merit. In principle, the merit function can be chosen arbitrarily and applied to multiple dimensions without loss of generality. Here we set the CQS as the merit for light-rendering capability. This is because it employs a set of color samples all of higher chroma and adopts a more uniform CIELAB color space than the CRI. The major improvements are that the CQS takes into account observer preferences by reflecting the differences between hue and saturation shifts and by using the rms of color differences to ensure that large shifts in any color sample can be adequately incorporated in the overall score.

          f=w×CQS+(1w)×LE,subject to the constrain: weight w[0,1].
(8)

2.4. Merit Analysis

The weight factor w provides an additional freedom for the user to determine the operation point among different figures of merit. Equation (8) constitutes a two-dimensional optimal boundary (Pareto front, PF) between the CQS and LE. Different weight values w profile a series of locus of operating points with different prescribed (TCC, Ta), as shown in Fig. 5(a)
Fig. 5 (a) Illustration of the Pareto fronts (PF) for different TCC on the CQS-LE plane. (b) The flowchart of SA1. Either end point P0 or P1 located within quadrant III will lead to an unacceptable performance as PF3. The curve with end points located within quadrant II and IV, like PF2, should be confirmed the operation portion (red curve).
. For the sake of computation efficiency, we proposed two sampling methods, SA1 and SA2, to analyze respectively the cluster performance among the CQS and LE. The concept of merit analysis is similar to the aberration analysis for different fields of view (usually at object height of 0, 0.5, 0.7, and 1) in lens design [21

21. W. J. Smith, Modern Lens Design, 2nd ed. (McGraw-Hill, 2005).

].

The principle of SA1 is based on the curve-fitting (CF) approach. The first step is to examine the locations of both extreme points (P0, P1) at the optimal boundary, where P0 (w = 0) represents the efficiency mode where all the weight is attributed toward the LE. P1 (w = 1) thus represents the quality mode associated with all weighting CQS. If either end P0 or P1 is located at quadrant III, that means the CQS and LE fail to satisfy simultaneously the user-defined specification [quadrant I, defined by the minimum LEm and minimum CQSm]. The reason is due to the optimal boundary that always exhibits a tradeoff relation and is not likely to appear as a positive slope. If the Pareto front locus is profiled as PF1 or PF2, we must interpolate other points such as Pa and Pb to help us succeed in fitting the optimal boundary curve. The CF process is the same as that of the SF model in Eqs. (5)(7) but with a “single-input single-output” system. Taking PF2 as an example, presently the modeled curve overlaps partially with quadrant I, defining an operation portion (red curve) with two extreme ends whose weights can be estimated by establishing another SF model, f (P) = w, with four input points P 0, Pa, Pb, and P 1 as well as four output weights 0, a, b, and 1. As a consequence, an appropriate weight can be obtained from the proposed sampling method with a small number of sample points. The SA1 procedure is summarized as shown in Fig. 5(b).

Compared with curve-fitting method SA1, linear approximation is computationally efficient to determine the appropriate operating point, as shown in the dashed lines of Fig. 5(a). In this way, we assume P0P1¯ already crosses quadrant I. For no particular reason, we choose P0 (the highest LE mode or efficiency mode) as the starting point. The increment rate of the CQS (CQS 1/0) at the expense of the LE decrement (LE 1/0) can be defined as in Eq. (9),

CQS1/0=CQS1CQS0CQS0,  and   LE1/0=LE1LE0LE0.
(9)

For an arbitrary point Pc (CQSc, LEc) located on the line P0P1¯, the weight c is determined by Eq. (10),
c=(CQScCQS0)/CQS0CQS1/0=(LEcLE0)/LE0LE1/0,
(10)
where c also indicates the increasing rate of CQS 1/0 as well as the decreasing rate of LE 1/0. Linear approximation SA2 is a fast way to find the optimal operating point at the expense of a precise estimation of the weight value. Meanwhile, this method might face risk as in the PF2 case that the real Pareto front curve is cross quadrant I, but the linear approximation P0P1¯ case does not.

2.5. Judgment

Up to this point, an optimal operation has been determined under an appropriate weighting value w. However, another aspect that must be taken into account in the framework of prototype is the margin analysis. During the course of spectral synthesis, in a situation where one LED is dimming to an extremely low level, we can possibly remove it without affecting system performance. Likewise, we can add the number of LEDs to allow the operation within adequate margins. Either scheme has good correspondence with the skill the lens design uses as follows [22

22. M. Laikin, Lens Design, 4th ed. (CRC Press, 2006).

]:

  • 1. Among the operating wavelengths, add a new available wavelength and vary its emission power to analyze the merits (CQS and LE) again. It is usual to insert a wavelength at the large interval between peak wavelengths.
  • 2. Replace two or more single-color LEDs by a phosphor-converted LED, or vice versa. If there is a remarkable performance advance in any kind of LEDs, try to adopt it.
  • 3. Split an operating wavelength of too-high emission power into two adjacent wavelengths. This may be useful to avoid dangerous operation in a tiny margin of the requirements.

Once the optimal boundary crosses the user-defined specification (quadrant I), we can fix an appropriate weight value by using SA1 or SA2 for the merit function accordingly.

2.6. Tolerance Analysis

Finally, the designer can introduce a small perturbation to each parameter sequentially (λ0,i, Δλi, Ii, Ta) and observe the corresponding change. It is noted that the presented technique merely confines the discussion to the spectral range––it is not likely to predict the light field changed by the geometric deviation such as an LED package error or assembly misalignment. A possible compensation mechanism that constantly measures the SPD on the illuminated plane and gives feedbacks to drive currents might be helpful to improve the tolerance margin in the LED cluster [3

3. E. F. Schubert, Light-emitting Diodes, 2nd ed. (Cambridge University Press, 2006).

].

3. Design Example

In order to validate the devised model, here we setup a platform of a pentachromatic white source composed of four single-color LEDs and a phosphor-converted CW LED (Excellence Opto. Inc., EOQ5P), respectively. With ambient temperature Ta = 300 K and driving currents I = 20 mA, the LED spectra are shown in Fig. 6
Fig. 6 Spectra of red (R), green (G), blue (B), amber (A), and CW LEDs at ambient temperature Ta of 300 K with all driving currents of 20 mA. The corresponding chromaticity points and specifications are also shown in the figure. The deriving currents were controlled by a pulse-width modulation (PWM) approach with a pulse width of 6.66 ms at differences of 0.04–0.06 ms for each gray level (a total of 128 gray levels).
. An adequate layout of an LED arrangement and optics by a first-order design was considered to deliver a uniform illumination [23

23. I. Moreno, M. Avendaño-Alejo, and R. I. Tzonchev, “Designing light-emitting diode arrays for uniform near-field irradiance,” Appl. Opt. 45(10), 2265–2272 (2006). [CrossRef] [PubMed]

]. Because of a low level of driving currents, the SPD modeling for each color LED can be assumed to satisfy the scalability and addictivity in a color mixing scheme [24

24. R. S. Berns, “A genetic approach to color modeling,” Color Res. Appl. 22(5), 318–325 (1997). [CrossRef]

].

To fulfill the modeling through aforementioned Sections 2.2–2.6, four operational SPDs with two extreme points (P0, P1) under TCC = 3000 K and 6500 K are verified, as shown in Fig. 7
Fig. 7 Spectral comparisons of simulations and experiments for P 0 and P 1 at TCC of 6500 K and 3000 K. The simulated spectra closely matched the measurements in spite of baring a few peak deviations.
. The simulation results are in close agreement with the experimental measurements within Δxy = 0.01 chromaticity deviation. Figure 8
Fig. 8 Illuminant environments at (a) CQS = 87 points for TCC = 6500 K and (b) CQS = 69 points for TCC = 3000 K show apparently different color rendering abilities.
features illuminant environments for different color temperatures (TCC = 3000 K and 6500 K), where the composite spectra from the LED matrix are digitally controlled by pulse-width modulation (PWM) with 128 gray levels.

In addition to experimental validation, more insight can be pursued for smart lighting operation. Here we assume the minimum requirements for color rendering CQSm = 80 points and LEm = 60 lm/W. Based on the linear approximation SA2 method, the loci of R/G/B, R/G/B/A, and R/G/B/A/CW are plotted in Fig. 9
Fig. 9 SA2 results of (a) R/G/B (black curve), R/G/B/A, and (b) R/G/B/A/CW clusters aimed to P 1 and P 0 for full range of TCC from 1000 K to 10000 K.
. The black curve in Fig. 9(a) depicts the referenced single solution of an R/G/B cluster for each color temperature. By adding amber (A) to the R/G/B cluster, we can improve an average of 50% CQS without too much loss of LE. The result is generally in agreement with the concept that a wide spectrum would improve the color-rendering performance. On the other hand, the contribution of an additional CW LED is depicted in Fig. 9(b), which could further increase 5% in CQS and 20% in LE over the full range of color temperature. The reason is due to the fact that a CW LED associated with high efficiency offers a good option to replace the function of blue color. The detail will be analyzed at the end of this section.

The following is to determine both operating windows for R/G/B/A and R/G/B/A/CW clusters. We first set point P0 (w = 0) that lies in the efficient mode as the starting point for each TCC. Figure 10
Fig. 10 Results of CQS1/0 and LE1/0 for R/G/B/A and R/G/B/A/CW clusters. By using SA2 analysis, R/G/B/A/CW can extend the operation window.
shows the information about CQS1/0 and LE1/0 that indicates the increment rate of CQS at a sacrifice of the decrement rate of LE in P0. Because the R/G/B/A cases for all TCC are located at the right top corner, the designer would undoubtedly chose a high weight value (w~1) to boost the color rendering ability at little expense in cluster efficiency. This action is equivalent to drive P0 approaching P1 along the straight line in Fig. 9(a). Nevertheless, the R/G/B/A cluster still suffers a stringent operating window of TCC (2800–3000 K), which precludes its use in intelligent lighting applications.

Compared with the R/G/B/A cluster, the addition of a cold-white (CW) LED extends the operation window throughout the entire color temperature range. To prove this we select starting point P0 at TCC = 3000 K, where CQS0 = 66.8 points (unqualified) and LE0 = 66.7 lm/W [refer to Fig. 9(b)]. The correspondin information for CQS1/0 = 34.3% and LE1/0 = −2.4% at the same point P0 can be found in Fig. 10. It is easy to take the above parameters into Eq. (10) and derive an appropriate weight of 0.79 to fulfill the requirement via increasing the CQS value to 85 points at a sacrifice of 1.3 lm/W. Generally, the weighting value can be conducted to compare between CQS1/0 and LE1/0. That means the balance condition of CQS 1/0 ≈−LE 1/0 at TCC of 5200 K in Fig. 10 can be regarded as a turning point for the weight selection. Taking the R/G/B/A/CW combination for example, it is logical to approach the requirements by setting a large weighting value (w > 0.5) for TCC < 5200K, and vice versa.

At this point we can successfully determine the operation point with the proposed methodology and set an optimal lighting environment for an R/G/B/A/CW system as shown in Fig. 11
Fig. 11 (a) Values of CQS and LE, and (b) the stacked emission power ratio versus correlated color temperature for an optimized R/G/B/A/CW design (CQSm = 80 points and LEm = 60 lm/W). The operation window has been extended to 2600 K < TCC < 8500 K with the selected weight using SA2 for each TCC. In fact, the operation window is restricted by the CQS rather than efficiency. The result shows that high efficiency CW LED is a good substitute for a blue LED.
. The operation window is extended to span 2600–8500 K with user-defined requirements of CQSm = 80 points and LEm = 60 lm/W, which would shrink under more severe lighting requirements accordingly (e.g., the operation window of 3200–5600 K for CQSm = 90 points and LEm = 64 lm/W).

Based on Fig. 11, we find that when the correlated color temperature is less than 6400 K, the power ratio is mainly governed by the light quality requirement (high weighting factor), and each component has a comparable amount. On the other hand, when the operation temperature is higher than 6400 K, the efficiency requirement (high weighting factor) is dominated and contributed to by a CW LED. The combination of LED clusters reduces to R/G/CW for 6400 K < TCC < 8500 K as shown in Fig. 11(b). Within the operation window of 2600–8500 K, the result in Fig. 11(b) also indicates the function of the blue LED has been replaced by the CW light, so we can discard it from the cluster for most general lighting applications.

4. Conclusion

A novel LED mixing scheme analogous to the conventional lens design process has been proposed. The algorithm enables the users to determine easily the optimal LED setup to meet requirements such as light efficiency, color quality, or other figures of merit over a wide range of color temperatures. The procedure includes six steps––(2.1) initial system, (2.2) define boundary condition, (2.3) optimization, (2.4) merit analysis, (2.5) judgment, and (2.6) tolerance analysis, and each step has been considered and validated in detail by an experimental platform. The design example of an R/G/A/CW cluster can extend the operation window to 2600 K < TCC < 8500 K for the requirements of CQSm = 80 points and LEm = 60 lm/W. Due to its simplicity and versatility, the proposed technique certainly has a promising impact on rapid prototyping and other specialized features for lighting applications.

Acknowledgment

The authors thank Mr. SB Chiang and SM Tasi for their technical support and discussion. This work was financially supported by the National Science Council, Taiwan, under grant NSC 99-2221-E-009-067-MY3.

References and links

1.

J. K. Kim and E. F. Schubert, “Transcending the replacement paradigm of solid-state lighting,” Opt. Express 16(26), 21835–21842 (2008), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-16-26-21835. [CrossRef] [PubMed]

2.

E. F. Schubert and J. K. Kim, “Solid-state light sources getting smart,” Science 308(5726), 1274–1278 (2005). [CrossRef] [PubMed]

3.

E. F. Schubert, Light-emitting Diodes, 2nd ed. (Cambridge University Press, 2006).

4.

A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, R. Gaska, and M. S. Shur, “Optimization of white polychromatic semiconductor lamps,” Appl. Phys. Lett. 80(2), 234–236 (2002). [CrossRef]

5.

G. He and L. Zheng, “Color temperature tunable white-light light-emitting diode clusters with high color rendering index,” Appl. Opt. 49(24), 4670–4676 (2010). [CrossRef] [PubMed]

6.

G. He and L. Zheng, “White-light LED clusters with high color rendering,” Opt. Lett. 35(17), 2955–2957 (2010). [CrossRef] [PubMed]

7.

Y. Ohno, “Color rendering and luminous efficacy of white LED spectra,” Proc. SPIE 5530, 88–98 (2004). [CrossRef]

8.

W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010). [CrossRef]

9.

M. S. Rea and J. P. Freyssinier-Nova, “Color rendering: a tale of two metrics,” Color Res. Appl. 33(3), 192–202 (2008). [CrossRef]

10.

A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, H. Vaitkevičius, P. Vitta, and M. S. Shur, “Statistical approach to color quality of solid-state lamps,” IEEE J. Sel. Top. Quantum Electron. 15(6), 1753–1762 (2009). [CrossRef]

11.

E. F. Schubert, J. K. Kim, H. Luo, and J.-Q. Xi, “Solid-state lighting––a benevolent technology,” Rep. Prog. Phys. 69(12), 3069–3099(2006). [CrossRef]

12.

Epistar Corporation, Taiwan, General LED product catalog (2010).

13.

Toyoda Gosei Corporation, Japan, LED product catalog (2010).

14.

R. Kingslake, Lens Design Fundamentals (Academic Press, 1978).

15.

A. Žukauskas, R. Vaicekauskas, and M. S. Shur, “Solid-state lamps with optimized color saturation ability,” Opt. Express 18(3), 2287–2295 (2010), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-18-3-2287. [CrossRef] [PubMed]

16.

R. Mirhosseini, M. F. Schubert, S. Chhajed, J. Cho, J. K. Kim, and E. F. Schubert, “Improved color rendering and luminous efficacy in phosphor-converted white light-emitting diodes by use of dual-blue emitting active regions,” Opt. Express 17(13), 10806–10813 (2009), http://www.opticsinfobase.org/abstract.cfm?URI=oe-17-13-10806. [CrossRef] [PubMed]

17.

S. Chhajed, Y. Xi, Y. L. Li, T. Gessmann, and E. F. Schubert, “Influence of junction temperature on chromaticity and color rendering properties of trichromatic white light source based on light-emitting diodes,” J. Appl. Phys. 97(5), 054506 (2005). [CrossRef]

18.

G. Wyszecki, and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data, and Formulae (Wiley, 2000).

19.

J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing (Prentice Hall, 1997).

20.

R. L. Haupt, and S. E. Haupt, Practical Genetic Algorithms, 2nd ed. (John Wiley, 2004).

21.

W. J. Smith, Modern Lens Design, 2nd ed. (McGraw-Hill, 2005).

22.

M. Laikin, Lens Design, 4th ed. (CRC Press, 2006).

23.

I. Moreno, M. Avendaño-Alejo, and R. I. Tzonchev, “Designing light-emitting diode arrays for uniform near-field irradiance,” Appl. Opt. 45(10), 2265–2272 (2006). [CrossRef] [PubMed]

24.

R. S. Berns, “A genetic approach to color modeling,” Color Res. Appl. 22(5), 318–325 (1997). [CrossRef]

OCIS Codes
(230.3670) Optical devices : Light-emitting diodes
(330.1690) Vision, color, and visual optics : Color
(330.1715) Vision, color, and visual optics : Color, rendering and metamerism

ToC Category:
Light-Emitting Diodes

History
Original Manuscript: April 11, 2011
Revised Manuscript: May 18, 2011
Manuscript Accepted: May 23, 2011
Published: June 9, 2011

Virtual Issues
Vol. 6, Iss. 8 Virtual Journal for Biomedical Optics

Citation
Ming-Chin Chien and Chung-Hao Tien, "Cluster LEDs mixing optimization by lens design techniques," Opt. Express 19, A804-A817 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-S4-A804


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References

  1. J. K. Kim and E. F. Schubert, “Transcending the replacement paradigm of solid-state lighting,” Opt. Express 16(26), 21835–21842 (2008), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-16-26-21835 . [CrossRef] [PubMed]
  2. E. F. Schubert and J. K. Kim, “Solid-state light sources getting smart,” Science 308(5726), 1274–1278 (2005). [CrossRef] [PubMed]
  3. E. F. Schubert, Light-emitting Diodes, 2nd ed. (Cambridge University Press, 2006).
  4. A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, R. Gaska, and M. S. Shur, “Optimization of white polychromatic semiconductor lamps,” Appl. Phys. Lett. 80(2), 234–236 (2002). [CrossRef]
  5. G. He and L. Zheng, “Color temperature tunable white-light light-emitting diode clusters with high color rendering index,” Appl. Opt. 49(24), 4670–4676 (2010). [CrossRef] [PubMed]
  6. G. He and L. Zheng, “White-light LED clusters with high color rendering,” Opt. Lett. 35(17), 2955–2957 (2010). [CrossRef] [PubMed]
  7. Y. Ohno, “Color rendering and luminous efficacy of white LED spectra,” Proc. SPIE 5530, 88–98 (2004). [CrossRef]
  8. W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010). [CrossRef]
  9. M. S. Rea and J. P. Freyssinier-Nova, “Color rendering: a tale of two metrics,” Color Res. Appl. 33(3), 192–202 (2008). [CrossRef]
  10. A. Žukauskas, R. Vaicekauskas, F. Ivanauskas, H. Vaitkevičius, P. Vitta, and M. S. Shur, “Statistical approach to color quality of solid-state lamps,” IEEE J. Sel. Top. Quantum Electron. 15(6), 1753–1762 (2009). [CrossRef]
  11. E. F. Schubert, J. K. Kim, H. Luo, and J.-Q. Xi, “Solid-state lighting––a benevolent technology,” Rep. Prog. Phys. 69(12), 3069–3099(2006). [CrossRef]
  12. Epistar Corporation, Taiwan, General LED product catalog (2010).
  13. Toyoda Gosei Corporation, Japan, LED product catalog (2010).
  14. R. Kingslake, Lens Design Fundamentals (Academic Press, 1978).
  15. A. Žukauskas, R. Vaicekauskas, and M. S. Shur, “Solid-state lamps with optimized color saturation ability,” Opt. Express 18(3), 2287–2295 (2010), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-18-3-2287 . [CrossRef] [PubMed]
  16. R. Mirhosseini, M. F. Schubert, S. Chhajed, J. Cho, J. K. Kim, and E. F. Schubert, “Improved color rendering and luminous efficacy in phosphor-converted white light-emitting diodes by use of dual-blue emitting active regions,” Opt. Express 17(13), 10806–10813 (2009), http://www.opticsinfobase.org/abstract.cfm?URI=oe-17-13-10806 . [CrossRef] [PubMed]
  17. S. Chhajed, Y. Xi, Y. L. Li, T. Gessmann, and E. F. Schubert, “Influence of junction temperature on chromaticity and color rendering properties of trichromatic white light source based on light-emitting diodes,” J. Appl. Phys. 97(5), 054506 (2005). [CrossRef]
  18. G. Wyszecki, and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data, and Formulae (Wiley, 2000).
  19. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing (Prentice Hall, 1997).
  20. R. L. Haupt, and S. E. Haupt, Practical Genetic Algorithms, 2nd ed. (John Wiley, 2004).
  21. W. J. Smith, Modern Lens Design, 2nd ed. (McGraw-Hill, 2005).
  22. M. Laikin, Lens Design, 4th ed. (CRC Press, 2006).
  23. I. Moreno, M. Avendaño-Alejo, and R. I. Tzonchev, “Designing light-emitting diode arrays for uniform near-field irradiance,” Appl. Opt. 45(10), 2265–2272 (2006). [CrossRef] [PubMed]
  24. R. S. Berns, “A genetic approach to color modeling,” Color Res. Appl. 22(5), 318–325 (1997). [CrossRef]

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