Note: In situ parameter extraction from a nonlinear dynamic model for electrical characterization of organic light emitting diodes M. C. Yoon, S. H. Choi, Y.-J. Kim, G.-T. Kim, and T.-W. Yoon Citation: Review of Scientific Instruments 85, 116102 (2014); doi: 10.1063/1.4901223 View online: http://dx.doi.org/10.1063/1.4901223 View Table of Contents: http://scitation.aip.org/content/aip/journal/rsi/85/11?ver=pdfcov Published by the AIP Publishing Articles you may be interested in Highly power efficient organic light-emitting diodes with a p -doping layer Appl. Phys. Lett. 89, 253504 (2006); 10.1063/1.2405856 Organic light-emitting diodes based on a cohost electron transporting composite Appl. Phys. Lett. 88, 113510 (2006); 10.1063/1.2178409 Electrical conduction in light-emitting organic polymer Schottky diodes J. Appl. Phys. 98, 124504 (2005); 10.1063/1.2143117 Negative capacitance in organic light-emitting diodes Appl. Phys. Lett. 86, 073509 (2005); 10.1063/1.1865346 Conducting fluorocarbon coatings for organic light-emitting diodes Appl. Phys. Lett. 84, 4032 (2004); 10.1063/1.1751220
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REVIEW OF SCIENTIFIC INSTRUMENTS 85, 116102 (2014)
Note: In situ parameter extraction from a nonlinear dynamic model for electrical characterization of organic light emitting diodes M. C. Yoon,1 S. H. Choi,2,a) Y.-J. Kim,3,a) G.-T. Kim,1 and T.-W. Yoon1,b) 1
School of Electrical Engineering, Korea University, Seoul 136-713, South Korea Robot Space Control Lab, Korea Institute of Industrial Technology, Ansan 426-171, South Korea 3 Technology Center, Samsung Mobile Display Co., Ltd., Yongin 446-711, South Korea 2
(Received 30 June 2014; accepted 27 October 2014; published online 11 November 2014) This Note presents a nonlinear device model for organic light emitting diodes (OLEDs), which can describe dynamic and static characteristics of OLEDs consistently. The parameters of the proposed model are estimated by using a particle swarm optimization algorithm. Some of the resulting parameters relate with physical characteristics of OLEDs. With only one set of experiments leading to a time response of an OLED device, this nonlinear model, together with all the parameters, is obtained, which can be a big advantage for the fast quality control of the OLEDs. © 2014 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4901223] Organic light emitting diodes or OLEDs are widely used in displays of mobile devices. Moreover, OLEDs receive attention as a next generation display because of many advantages such as higher efficiency, lower cost of production, and flexibility.1 The main issues of the light emission from an OLED include the carrier injection mechanism, the recombination process, and the transport mechanism. These are all related with some electrical characteristics of the OLED. In this Note, the characteristics of the OLED are modeled in a similar way to P-N junctions, as OLEDs and P-N junctions have similar structures. The physical parameters for a P-N junction include the minority carrier lifetime (τ m ), the ideality factor (n), and the reverse saturation current (IS0 ),2 these parameters are also considered for the OLED. Different methods have been employed for estimating different electrical parameters. The low frequency impedance spectroscopy or steady-state I-V measurements are mainly used to estimate IS0 and n. Several methods exist for estimating τ m , which require special equipment such as photo detectors or impedance meters.3 Hence employing existing methods for estimating all the important OLED parameters is costly and laborious, requiring separate sets of experiments and measurements. The model proposed in this Note is nonlinear, which contains IS0 , n, and τ m as its parameters. A particle swam optimization (PSO) algorithm is employed to estimate these parameters using a single set of measurement data. As a consequence, this nonlinear model efficiently provides the in situ characterization of the OLED parameters. All the parameters including IS0 , n, and τ m are estimated at once. This is demonstrated by experimental results. The OLED in question consists of several thin layers of films which are a cathode, an electron injection layer (EIL), an electron transport layer (ETL), an emission layer (EL), a
a) This research was performed while S. H. Choi and Y.-J. Kim were at School
of Electrical Engineering, Korea University, Seoul 136-713, South Korea.
b) E-mail: [email protected].
0034-6748/2014/85(11)/116102/3/$30.00
hole transport layer (HTL), a hole injection layer (HIL), and an anode. The host material of the EL layer has p-type characteristics, and thus holes are the majority carriers. The dopant material of the EL has n-type characteristics, and thus electrons are the majority carriers. Hence the EL is regarded as an equivalent P-N junction in the proposed model. If the voltage across the EL is larger than the threshold voltage, the EL functions like an RC circuit as does the PN junction.2 The structure of an OLED is depicted in Figure 1, where RD and CD denote the diode resistor and capacitor of EL. RPHI , RPHT , RPET , and RPEI denote the parasitic resistors of the HIL, HTL, ETL, and EIL. In addition, the proposed model contains CP to account for the capacitance between electrodes and packaging material. An equivalent circuit of this OLED model is given in Figure 2, where VD , VOLED , and IOLED denote the voltage across the EL, the voltage across the OLED and the current flowing into the OLED, respectively, and RP is the sum of RPHI , RPHT , RPET , and RPEI . RD and CD are given by RD = RD0 exp(−αVD ),
(1)
CD = CD0 exp(αVD ),
(2)
RD0 = α/IS0 ,
(3)
RD0 CD0 = τm ,
(4)
α = e/nkB T ,
(5)
where
with e,kB , and T being the unit charge of an electron, the Boltzmann constant and the absolute temperature. Note that RD0 and CD0 are not affected by VD , as IS0 is independent of VD . It ensues from (1) and (2), and the equivalent circuit in Figure 2 that the OLED model is written as the following
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© 2014 AIP Publishing LLC
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Rev. Sci. Instrum. 85, 116102 (2014)
FIG. 3. Output (VOLED ) of the OLED (solid) and the model output (dotted); the maximum value of residual is less than 0.8% of the OLED output. FIG. 1. Structure of OLED.
nonlinear differential equations: − VD I V d V = OLED − OLED , dt OLED CP C P RP
(6)
1≤i≤N
VD − VD )exp(−αVD ) d (V V = OLED − . dt D RP CD0 (1 + αVD ) RD0 CD0 (1 + αVD ) (7) The OLED to be modeled in this Note is an un-aged device (R2B-C235) provided by Samsung Display Co., Ltd. For the measurement purpose, a resistor RL is connected in series with the OLED. To the resulting circuit, the input voltage VI N is applied, and the voltage VOLED across the OLED is measured as the output, using a data acquisition board (NI PCI6115). The current IOLED flowing into the OLED can then be given by IOLED = (VI N − VOLED )/RL .
(8)
The room temperature is 20 ◦ C. The PSO algorithm suggested by Schwaab et al.4 is used to estimate the parameter vector θ consisting of RD0 , CD0 , RP , CP , and α. The PSO is a heuristic optimization algorithm which allows for improved parameter estimation with less computational efforts, when compared to the popular genetic algorithm.4 Let θ ij denote the ith candidate estimate for θ at the jth iteration, for 1 ≤ i ≤ N with N being the number of candidate estimates at each iteration; the best estimate θ ik∗ of ith candidate is given by ˆ θ ik∗ = arg max h(θ ij ) = arg max h(θ), 1≤j ≤k
(9)
i i ˆ θ∈{θ (k−1)∗ ,θ k }
ˆ ˆ where defined as h(θ) h(θ) is the objective function 2 ˆ ˆ ˆ ˆ = ||VOLED (t) − VOLED (θ , t)|| with VOLED (θ ) being the t
FIG. 2. Equivalent circuit.
value of VOLED evaluated using the estimate θˆ . Then the best estimate θ ∗k at the kth iteration is obtained by θ ∗k = arg max h θ ik∗ . (10) Having obtained the estimate θ ∗k from θ ij for 1 ≤ j ≤ k at the kth iteration, θ ik+1 is updated for the next iteration as follows: θ ik+1 = θ ik + c1i θ ik∗ − θ ik + c2i θ ∗k − θ ik , (11) where c1i is a random number between 0 and c1m , and c2i between 0 and c2m . The best estimate θ ∗k+1 at the (k+1)th iteration is then obtained using the same procedure given above. This iteration procedure is repeated until the objective function h(θ ∗k ) converges. In this paper, the algorithm is stopped at the 10th iteration, i.e., θ ∗10 is the final estimate resulting from the PSO algorithm. In the estimation, N, c1m , and c2m are set to be 25, 2, and 2, respectively. A PRBS (Pseudo Random Binary Sequence) signal is selected for the input VI N ; the PRBS signal is a sequence of width modulated rectangular pulses, which has more frequency components than such signals as square waves.5 The values of RD0 , CD0 , RP , CP , and α are estimated to be 2.11 × 1013 , 1.00 × 10−22 , 6.01 × 103 , 2.33 × 10−9 , and 5.83, respectively. Figure 3 shows the output (VOLED ) of the OLED and that of the model computed on the basis of these estimated parameters. Note that, in all figures in this Note, solid and dotted lines denote the outputs of the OLED and the estimated model, respectively. The validity of the nonlinear model identified above is demonstrated here; both dynamic and static characteristics of the OLED are shown to be consistent with the corresponding experimental data. To verify the dynamic performance of the nonlinear model, the output of the resulting model is compared to that of the OLED on a variety of different operating points. Amongst the comparison results obtained, three cases (3 V, 3.5 V, 4 V for VOLED ) are shown in Figure 4, where the model outputs are clearly seen to match the real data consistently independent of the operating point. Note that the PRBS signals applied in these experiments are all different from the one used in Figure 3. The static characteristics of the model can also be verified. The I-V curve of the nonlinear model can be obtained analytically; this is compared to the experimental data in Figure 5, where the two curves are seen to be very close to each other. Note that the current is shown in log scale in
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FIG. 4. Comparison between the output of the OLED (dotted) and that of the model response (solid) on various operating points; the maximum value of residual is less than 0.5% of the OLED output.
the smaller figure inside the bigger one where the current is shown in linear scale. As a result of finding the dynamic model for OLED, some physical characteristics can be discussed with the parameters such as τ m , n, and IS0 , as these relate to the light emission mechanism. The estimates for τ m and n are obtained as 2.11 × 10−6 ms and 6.79 by (4) and (5), and IS0 can also be estimated to be 2.76 × 10−10 mA from (3). Note that the value of n is greater than 2, as is the case of the hetero-junction diode with the contribution of the
Rev. Sci. Instrum. 85, 116102 (2014)
off-center recombination processes;6 this result matches the structure of the EL which consists of two different materials. To check to see if the aging effect can be predicted by the parameters n and IS0 of the model, the modeling scheme proposed in this Note is applied to an aged device of the same type for which the light intensity dropped to 90% of the unaged device considered above. The parameters n and IS0 of this aged device are estimated to be 7.79 and 4.5 × 10−8 , which are greater than the corresponding values (6.79 and 2.76 × 10−10 ) for the un-aged device. Note that the estimate for IS0 is increased by a factor of 223. This means that the parameters n and especially IS0 can be used to estimate the life time of the device. To conclude, a new nonlinear model for the OLED is suggested in this Note, and its parameters are estimated using the PSO algorithm. Experiments show that the proposed nonlinear modeling method is effective when describing the dynamic and static behavior of the OLED, thereby predicting the physical characteristics of the device in terms of such parameters as τ m , n, and IS0 that relate to the lighting mechanism. With just a single set of experiments, all the important parameters and characteristics of the OLED can be obtained; this is in sharp contrast with conventional cases where different experiments are carried out for different characteristics of the device. Further investigation is to be conducted to reveal more closely the correlation between the model parameters and the device performance. It seems also necessary to develop a more efficient estimation scheme, as the PSO algorithm employed in this Note took rather a long time. This work was supported by Samsung Display Co., Ltd. 1 T.
Komoda, N. Ide, and J. Kido, J. Light Visual Environ. 32, 75 (2008).
2 S. O. Kasap, Principles of Electronic Materials and Devices (McGraw-Hill,
Columbus, 2006). K. Ahrenkiel, Solid-State Electron. 35, 239 (1992). 4 M. Schwaab, E. C. Biscaia, J. L. Monteiro, and J. C. Pinto, Chem. Eng. Sci. 63, 1542 (2008). 5 W. D. T. Davies, System Identification for Self-Adaptive Control (WileyInterscience, New York, 1970). 6 J. M. Shah, Y. L. Li, T. Gessmann, and E. F. Schubert, J. Appl. Phys. 94, 2627 (2003). 3 R.
FIG. 5. I-V curves of the OLED (solid) and the model (dotted); the maximum value of residual is less than 11% of the current flowing into the OLED for VOLED ≥ 2.75 (turn-on voltage).
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REVIEW OF SCIENTIFIC INSTRUMENTS 85, 116102 (2014)
Note: In situ parameter extraction from a nonlinear dynamic model for electrical characterization of organic light emitting diodes M. C. Yoon,1 S. H. Choi,2,a) Y.-J. Kim,3,a) G.-T. Kim,1 and T.-W. Yoon1,b) 1
School of Electrical Engineering, Korea University, Seoul 136-713, South Korea Robot Space Control Lab, Korea Institute of Industrial Technology, Ansan 426-171, South Korea 3 Technology Center, Samsung Mobile Display Co., Ltd., Yongin 446-711, South Korea 2
(Received 30 June 2014; accepted 27 October 2014; published online 11 November 2014) This Note presents a nonlinear device model for organic light emitting diodes (OLEDs), which can describe dynamic and static characteristics of OLEDs consistently. The parameters of the proposed model are estimated by using a particle swarm optimization algorithm. Some of the resulting parameters relate with physical characteristics of OLEDs. With only one set of experiments leading to a time response of an OLED device, this nonlinear model, together with all the parameters, is obtained, which can be a big advantage for the fast quality control of the OLEDs. © 2014 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4901223] Organic light emitting diodes or OLEDs are widely used in displays of mobile devices. Moreover, OLEDs receive attention as a next generation display because of many advantages such as higher efficiency, lower cost of production, and flexibility.1 The main issues of the light emission from an OLED include the carrier injection mechanism, the recombination process, and the transport mechanism. These are all related with some electrical characteristics of the OLED. In this Note, the characteristics of the OLED are modeled in a similar way to P-N junctions, as OLEDs and P-N junctions have similar structures. The physical parameters for a P-N junction include the minority carrier lifetime (τ m ), the ideality factor (n), and the reverse saturation current (IS0 ),2 these parameters are also considered for the OLED. Different methods have been employed for estimating different electrical parameters. The low frequency impedance spectroscopy or steady-state I-V measurements are mainly used to estimate IS0 and n. Several methods exist for estimating τ m , which require special equipment such as photo detectors or impedance meters.3 Hence employing existing methods for estimating all the important OLED parameters is costly and laborious, requiring separate sets of experiments and measurements. The model proposed in this Note is nonlinear, which contains IS0 , n, and τ m as its parameters. A particle swam optimization (PSO) algorithm is employed to estimate these parameters using a single set of measurement data. As a consequence, this nonlinear model efficiently provides the in situ characterization of the OLED parameters. All the parameters including IS0 , n, and τ m are estimated at once. This is demonstrated by experimental results. The OLED in question consists of several thin layers of films which are a cathode, an electron injection layer (EIL), an electron transport layer (ETL), an emission layer (EL), a
a) This research was performed while S. H. Choi and Y.-J. Kim were at School
of Electrical Engineering, Korea University, Seoul 136-713, South Korea.
b) E-mail: [email protected].
0034-6748/2014/85(11)/116102/3/$30.00
hole transport layer (HTL), a hole injection layer (HIL), and an anode. The host material of the EL layer has p-type characteristics, and thus holes are the majority carriers. The dopant material of the EL has n-type characteristics, and thus electrons are the majority carriers. Hence the EL is regarded as an equivalent P-N junction in the proposed model. If the voltage across the EL is larger than the threshold voltage, the EL functions like an RC circuit as does the PN junction.2 The structure of an OLED is depicted in Figure 1, where RD and CD denote the diode resistor and capacitor of EL. RPHI , RPHT , RPET , and RPEI denote the parasitic resistors of the HIL, HTL, ETL, and EIL. In addition, the proposed model contains CP to account for the capacitance between electrodes and packaging material. An equivalent circuit of this OLED model is given in Figure 2, where VD , VOLED , and IOLED denote the voltage across the EL, the voltage across the OLED and the current flowing into the OLED, respectively, and RP is the sum of RPHI , RPHT , RPET , and RPEI . RD and CD are given by RD = RD0 exp(−αVD ),
(1)
CD = CD0 exp(αVD ),
(2)
RD0 = α/IS0 ,
(3)
RD0 CD0 = τm ,
(4)
α = e/nkB T ,
(5)
where
with e,kB , and T being the unit charge of an electron, the Boltzmann constant and the absolute temperature. Note that RD0 and CD0 are not affected by VD , as IS0 is independent of VD . It ensues from (1) and (2), and the equivalent circuit in Figure 2 that the OLED model is written as the following
85, 116102-1
© 2014 AIP Publishing LLC
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Rev. Sci. Instrum. 85, 116102 (2014)
FIG. 3. Output (VOLED ) of the OLED (solid) and the model output (dotted); the maximum value of residual is less than 0.8% of the OLED output. FIG. 1. Structure of OLED.
nonlinear differential equations: − VD I V d V = OLED − OLED , dt OLED CP C P RP
(6)
1≤i≤N
VD − VD )exp(−αVD ) d (V V = OLED − . dt D RP CD0 (1 + αVD ) RD0 CD0 (1 + αVD ) (7) The OLED to be modeled in this Note is an un-aged device (R2B-C235) provided by Samsung Display Co., Ltd. For the measurement purpose, a resistor RL is connected in series with the OLED. To the resulting circuit, the input voltage VI N is applied, and the voltage VOLED across the OLED is measured as the output, using a data acquisition board (NI PCI6115). The current IOLED flowing into the OLED can then be given by IOLED = (VI N − VOLED )/RL .
(8)
The room temperature is 20 ◦ C. The PSO algorithm suggested by Schwaab et al.4 is used to estimate the parameter vector θ consisting of RD0 , CD0 , RP , CP , and α. The PSO is a heuristic optimization algorithm which allows for improved parameter estimation with less computational efforts, when compared to the popular genetic algorithm.4 Let θ ij denote the ith candidate estimate for θ at the jth iteration, for 1 ≤ i ≤ N with N being the number of candidate estimates at each iteration; the best estimate θ ik∗ of ith candidate is given by ˆ θ ik∗ = arg max h(θ ij ) = arg max h(θ), 1≤j ≤k
(9)
i i ˆ θ∈{θ (k−1)∗ ,θ k }
ˆ ˆ where defined as h(θ) h(θ) is the objective function 2 ˆ ˆ ˆ ˆ = ||VOLED (t) − VOLED (θ , t)|| with VOLED (θ ) being the t
FIG. 2. Equivalent circuit.
value of VOLED evaluated using the estimate θˆ . Then the best estimate θ ∗k at the kth iteration is obtained by θ ∗k = arg max h θ ik∗ . (10) Having obtained the estimate θ ∗k from θ ij for 1 ≤ j ≤ k at the kth iteration, θ ik+1 is updated for the next iteration as follows: θ ik+1 = θ ik + c1i θ ik∗ − θ ik + c2i θ ∗k − θ ik , (11) where c1i is a random number between 0 and c1m , and c2i between 0 and c2m . The best estimate θ ∗k+1 at the (k+1)th iteration is then obtained using the same procedure given above. This iteration procedure is repeated until the objective function h(θ ∗k ) converges. In this paper, the algorithm is stopped at the 10th iteration, i.e., θ ∗10 is the final estimate resulting from the PSO algorithm. In the estimation, N, c1m , and c2m are set to be 25, 2, and 2, respectively. A PRBS (Pseudo Random Binary Sequence) signal is selected for the input VI N ; the PRBS signal is a sequence of width modulated rectangular pulses, which has more frequency components than such signals as square waves.5 The values of RD0 , CD0 , RP , CP , and α are estimated to be 2.11 × 1013 , 1.00 × 10−22 , 6.01 × 103 , 2.33 × 10−9 , and 5.83, respectively. Figure 3 shows the output (VOLED ) of the OLED and that of the model computed on the basis of these estimated parameters. Note that, in all figures in this Note, solid and dotted lines denote the outputs of the OLED and the estimated model, respectively. The validity of the nonlinear model identified above is demonstrated here; both dynamic and static characteristics of the OLED are shown to be consistent with the corresponding experimental data. To verify the dynamic performance of the nonlinear model, the output of the resulting model is compared to that of the OLED on a variety of different operating points. Amongst the comparison results obtained, three cases (3 V, 3.5 V, 4 V for VOLED ) are shown in Figure 4, where the model outputs are clearly seen to match the real data consistently independent of the operating point. Note that the PRBS signals applied in these experiments are all different from the one used in Figure 3. The static characteristics of the model can also be verified. The I-V curve of the nonlinear model can be obtained analytically; this is compared to the experimental data in Figure 5, where the two curves are seen to be very close to each other. Note that the current is shown in log scale in
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FIG. 4. Comparison between the output of the OLED (dotted) and that of the model response (solid) on various operating points; the maximum value of residual is less than 0.5% of the OLED output.
the smaller figure inside the bigger one where the current is shown in linear scale. As a result of finding the dynamic model for OLED, some physical characteristics can be discussed with the parameters such as τ m , n, and IS0 , as these relate to the light emission mechanism. The estimates for τ m and n are obtained as 2.11 × 10−6 ms and 6.79 by (4) and (5), and IS0 can also be estimated to be 2.76 × 10−10 mA from (3). Note that the value of n is greater than 2, as is the case of the hetero-junction diode with the contribution of the
Rev. Sci. Instrum. 85, 116102 (2014)
off-center recombination processes;6 this result matches the structure of the EL which consists of two different materials. To check to see if the aging effect can be predicted by the parameters n and IS0 of the model, the modeling scheme proposed in this Note is applied to an aged device of the same type for which the light intensity dropped to 90% of the unaged device considered above. The parameters n and IS0 of this aged device are estimated to be 7.79 and 4.5 × 10−8 , which are greater than the corresponding values (6.79 and 2.76 × 10−10 ) for the un-aged device. Note that the estimate for IS0 is increased by a factor of 223. This means that the parameters n and especially IS0 can be used to estimate the life time of the device. To conclude, a new nonlinear model for the OLED is suggested in this Note, and its parameters are estimated using the PSO algorithm. Experiments show that the proposed nonlinear modeling method is effective when describing the dynamic and static behavior of the OLED, thereby predicting the physical characteristics of the device in terms of such parameters as τ m , n, and IS0 that relate to the lighting mechanism. With just a single set of experiments, all the important parameters and characteristics of the OLED can be obtained; this is in sharp contrast with conventional cases where different experiments are carried out for different characteristics of the device. Further investigation is to be conducted to reveal more closely the correlation between the model parameters and the device performance. It seems also necessary to develop a more efficient estimation scheme, as the PSO algorithm employed in this Note took rather a long time. This work was supported by Samsung Display Co., Ltd. 1 T.
Komoda, N. Ide, and J. Kido, J. Light Visual Environ. 32, 75 (2008).
2 S. O. Kasap, Principles of Electronic Materials and Devices (McGraw-Hill,
Columbus, 2006). K. Ahrenkiel, Solid-State Electron. 35, 239 (1992). 4 M. Schwaab, E. C. Biscaia, J. L. Monteiro, and J. C. Pinto, Chem. Eng. Sci. 63, 1542 (2008). 5 W. D. T. Davies, System Identification for Self-Adaptive Control (WileyInterscience, New York, 1970). 6 J. M. Shah, Y. L. Li, T. Gessmann, and E. F. Schubert, J. Appl. Phys. 94, 2627 (2003). 3 R.
FIG. 5. I-V curves of the OLED (solid) and the model (dotted); the maximum value of residual is less than 11% of the current flowing into the OLED for VOLED ≥ 2.75 (turn-on voltage).
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