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jsmtgenesis

Advanced Monitoring of TMAH Solution

by jsmtgenesis

Authored by Michael MacEwan, Jingjing Wang, Eugene Shalyt, Chuannan Bai, Guang Liang, Vishal Parekh
Presented at the 12th Annual Symposium on Ultra Clean Processing of Semiconductor Surfaces in Brussels, Belgium, 2014.

ECI Technology, Totowa, NJ 07512, USA

Outline

  • Project Goal
  • Importance of TMAH in Semiconductor Processing
  • Analysis Method Development
    • TMAH
    • Carbonate
    • Surfactant
  • Field Process Data
  • Conclusion

Project Goals

Develop Metrology to Facilitate Process Improvement and Knowledge for TMAH Solutions.

Chemical Structure of TMAH

Tetra Methyl Ammonium Hydroxide – a non-metallic source of the Hydroxyde ion.

Advantages of TMAH

  • No alkali metals {Li, Na, K} used as potential contaminant
  • Used in positive photoresist developers
  • Anisotropy (111):(100) ~ 1:10 to 1:35
  • Does not significantly etch SiO2 or Al (bond wire safe)

TMAH Applications – Lithography

It desolves acidic photoresist after exposure

Rate is concentration dependent and slows over time.

TMAH Applications – Etch

Etch rate is effected by TMAH: rate increases at lower concentration.

Methods of Analysis for TMAH

Application requirements are 0.002 wt% accuracy.

Analysis of TMAH – pH

  • Examples of concentration change on pH:
  • 0.01 wt% change in TMAH ~ 0.1 pH change
  • 0.001 wt% change in TMAH ~ 0.01 pH change

Current on-line techniques (ISFET, Optical) have performance to 0.1 pH units – not enough sensitivity

Analysis of TMAH – Titration

Titration is effected by the presence of carbonate
40ppm CO32- will cause 0.006 wt% error

Analysis of TMAH – NIR

TMAH concentration range from 0-3 wt%

Error is within ±0.015 wt% for all samples – still not enough accuracy to achieve the goal.

Analysis of TMAH – Conductivity

Excellent linearity within process operating range

±0.001 wt% reproducibility and accuracy obtained by temperature correction.

Analysis of Carbonate – Spectroscopy

  • Surfactant does not interfere with CO32- analysis
  • Test sample contains trace levels of CO32-
  • Range of 0-40ppm CO32- is linear

Carbonate absorption in TMAH

CO32- increase significantly in unprotected sample over 14 hours

Analysis of Surfactant – Surface Tension

Surfactant in TMAH solution decreases the ST
ST measurement is not sensitive in 172-175 ppm range.

Analysis of Surfactant – CVS

CVS of TMAH-Surfactant sample – Method is sensitive to low level of surfactant change.

CVS Response for Surfactant Concentration

Increasing surfactant concentration has a stronger effect.

Proposed configuration of Analyzer

TMAH Etch Field Process Data

Conclusion

  1. A comprehensive suite of automated analytical methods has been developed to meet the challenges of modern semiconductor TMAH applications for Sigma Etch and PR development:
  2. TMAH/DI
  3. Carbonate
  4. Surfactant

Methods have been successfully field-validated.

Thank you for your attention.

Advanced Process Control Of Nickel Electrodeposition for Packaging in Semiconductor Industry

by jsmtgenesis

Authored by Eugene Shalyt, Jingjing Wang, Vishal Parekh, Michael MacEwan
Presented at the ElectroChemical Society’s 226th Biannual meeting in Cancun, Mexico, 2014.

ECI Technology, Totowa, NJ 07512, USA

Outline

  • Use of Nickel Electrodeposition in Packaging
  • Components of electrolyte and their function
  • Metrology approaches and field validation with specific focus on
    • Surfactant
    • Leached Photoresist
    • Sulfur-Bearing Additive and/or Breakdown Product
    • Nickel
  • Conclusions

Use of Ni Electrodeposition

Nickel is not used as a final layer. It is used as a diffusion barrier between Cu and other final layers: Sn and it’s alloys, Pd, and Au.

Barrier Properties of Ni Electrodeposit

Components of Electrolyte

Root Cause of Pitting

Pitting Remediation

  • Reduce amount of H2 bubbles
    • maintaining pH > 4 to reduce partial current of H2 formulation
      however Ni(OH)2 formation can occur above pH 4.5
  • Reduce lifetime of bubbles
    • Intense agitation
    • Use of surfactant/wetter to lower surface tension

Effect of Surfactant on Voltammogram

Suppression vs. Wetter Property

There is no link. Chemical can have both properties, none, or only one.

Measurement of Surfactant by Surface Tension

  • Response is not linear
  • Matrix and temperature dependent
  • Surface tension results cannot be used for precise measurements

Measurement of Surfactant by Titration

R- SO3–Na+ + R4NCl–  –> RSO3NR4 + NaCl

Measurement of Leached Photoresist

Field Data on Accumulation of PR

Effect of PR on Voltammogram

Effect of S Compound

S Compound

Source:

  • Added on purpose (Saccharine derivative)
  • Sulfamate breakdown product

Monitoring of S Compound

  • HPLC
  • Polarography
  • Stress Measurement
  • Cyclic Voltammetry
  • Spectroscopy (new)

Monitoring of S Component

Field Data for S Component

Monitoring of Ni

  • Complexometric titration
    • Slow, high cost of ownership
  • Spectroscopy
    • Realtime, non-reagent, low cost of ownership

UV-Vis Spectra of Ni Solutions

Ni @ 66 g/l
matrix components are varied within process range

Majority of spectral range is highly selective to Ni and free of interferences

Monitoring of Other Components

Conclusions

  • Comprehensive suite of automated analytical methods have been developed to meet the challenges of modern semiconductor applications:
  • Nickel
  • Boric
  • Anode Activator (Cl, Br)
  • pH
  • Surfactant
  • Leached Photoresist
  • Sulfur-Bearing Additive and/or Breakdown Product

In situ monitoring of components to control dilute wet chemistries

by jsmtgenesis

Materials loss per cleaning cycle must be limited in advanced semiconductor process flows. Using dilute chemistries is necessary to achieve this goal. Fab engineers need to be able to analyze the concentrations of each component in processing solutions in real time to ensure that the chemistries are always in a narrowly defined process window. This article describes how multivariate methods can be applied to analyze chemistries of concentrations ranging from conventional to ultra-dilute levels.

Material loss during cleaning is particularly problematic for advanced technology generations where there are very shallow junctions at the drain and source areas, and any excess removal of silicon can be detrimental to the resulting transistors. The International Technology Roadmap for Semiconductors (ITRS) therefore limits the materials loss of each cleaning cycle at 0.5Å for both oxide and silicon for the 65nm technology node, and 0.3Å for the 45nm technology node [1].

To achieve minimal materials loss, leading R&D groups have resorted to using dilute chemistries for cleaning processes. However, if the solution becomes overly dilute, then cleaning becomes ineffective. If the solution is too concentrated, then materials loss will exceed that permitted by the process specification. Therefore, concentrations of all components in a cleaning solution must be precisely controlled at all times. Effective in situ methods are required to monitor the concentrations of cleaning solutions in order to make necessary timely adjustments by spiking or diluting key components.

Currently adapted monitoring methods such as resistivity/conductivity techniques, though widely used for single component solutions (HF, for example), cannot differentiate contributions from different components in multicomponent solutions, such as SC1, SC2, DSP, etc. Titration and other laboratory techniques, though highly accurate and fully capable of differentiating co-existing components in the same solution, are relatively slow and not ideal for real-time control of solution concentrations. Conventional optical methods, typically measuring absorbance at discrete wavelengths corresponding to the absorption peaks of each component, worked well for legacy concentration ranges such as 1:1:5 or 1:1:20 SC1. But as the solutions become more dilute, the absorption signals are very weak even at the peaks, and the corresponding signal-to-noise ratio becomes very small. In some cases, absorption peaks of key components overlap each other. Such conventional optical methods lack the sensitivity mandatory for analyzing dilute chemistries. An improved method is necessary to analyze highly dilute solutions, such as 1:1:100 SC1 and 1:200 HF for advanced technology nodes.

To reliably analyze ultra-dilute multicomponent wet processing solutions in real time, the method must be noninvasive to both chemicals and wafers, meeting the automatic operation requirements for wafer fabs. The corresponding analytical results can then be fed back to the wet processing equipment through closed-loop control, so that the concentrations are maintained within process windows.

Broadband monitoring

A broadband absorption monitoring hardware system can analyze optical absorption signals of semiconductor wet processing solutions in the near-infrared (NIR, 700-2500nm) wavelength range. However, unlike simpler optical systems that measure at only a very limited number of wavelengths corresponding to absorption peaks, this hardware measures the absorption spectra of the chemical solutions across a wide range of wavelengths at very small intervals. The corresponding spectrum, which includes several hundreds of data points, is then analyzed by a proprietary software routine using a multivariate method [2] to resolve the limited number of unknowns.

In SC1 for example, the only unknowns are the concentrations of NH4OH and H2O2 balanced by DI water. The abundance of data points ensures high accuracy of the analysis; the redundancy of information compensates for noises associated with signal collection. Figure 1 shows examples of as-collected spectra by the hardware before being fed into the microprocessor for further processing.

The solutions were buffered HF, and each widely separated and differentiable spectrum corresponds to a distinctly different NH4F concentration. The two wavelength bands show examples of the discrete ranges typically used by conventional optical methods.

In many real fab cases, particularly for dilute solutions, minute changes in concentrations cannot be easily differentiated from raw data, rendering the conventional optical methods ineffective. In such cases, further processing of the spectrum by a multivariate method is mandatory to achieve the desired accuracy to determine the component concentrations. A schematic of such a setup is shown in Fig. 2a.

Light emitted from a high-stability broad-wavelength-range light source is diverted by a multiplexer into multiple channels through optical fibers to individual measuring modules. The setup is capable of measuring eight or more samples/points of different compositions.

Within each measuring module, piping (in a re-circulation loop or at point-of-use) of the wet processing equipment is encased by a flow-through cell (Fig. 2b).

Figure 02b. The flow-through cell hardware used to take the measurements

Incoming light is partially absorbed by the solution inside the piping before being collected by the optical fiber at the other side of the tubing. Light signals from multiple locations are then detected in sequence by a diode array detector before being forwarded to the computer for signal processing. The flow-through cell blocks stray light from the cleanroom and holds optical fibers onto precision locations and orientations, with respect to piping, to minimize measurement noises.

The flow-through cell is an integral part of the fluid path and does not alter the diameter from upstream or downstream piping. This eliminates concerns about optical distortion due to compression of fluid or release of dissolved air. No optics are in physical contact with the solutions in the piping and the reliability of the setup is expected to be high. Each analysis takes 1.5 sec, including software processing and collecting multiple full spectra at 0.4 millisec/spectrum.

Experimental results

One significant improvement of the current work over the authors’ earlier results [3, 4] is temperature correction. Small temperature variations at measurement points are unavoidable because of temperature changes in the solution and in the ambient, among other factors. Optical absorption signals are known to be extremely sensitive to minute temperature changes. For example, measurements of a dilute HF solution prepared at 620ppm varied perceptibly due to slight changes in temperature despite real-time correction. To resolve this problem, new spectral temperature correction software was developed that fundamentally minimizes measurement variability. The new spectral approach ensures that the measurement is insensitive to temperature variations over several degrees Celsius.

Multivariate measurements of NH4OH in ultra-dilute 1:1:500 SC1 solutions correlated better than 0.995 with the known concentrations of as-prepared solutions. Statistics of measuring H2O2 in SC1 showed similar reliability.

Figure 3 shows concentration changes of the two key components of an originally 1:1:50 SC1 mixture over a 16-hr period in a recirculation loop without active concentration control.

Figure 03. SC1 concentration changes over a 16-hr period in a recirculation loop without spiking/dilution, showing the ability to clearly resolve the components

NH4OH decreases much more slowly than H2O2, thus the latter should be replenished more frequently into the tank. Such different trends cannot be resolved by resistivity/conductivity analytical techniques since contributions from ammonium hydroxide and hydrogen peroxide cannot be differentiated.

The multivariate technique measured several different concentrations of dilute HF between 1000 and 3000ppm (corresponding to approximately 1:570 and 1:190 dilutions, respectively) over an extended period of time. The temperature of the measuring environment was not actively controlled, varying between 20° and 25°C. Results show that all datasets were within 2.5% of the known as-prepared concentrations, and the repeatability was better than 0.7% in all cases. In general, when measuring HF of a 50,000ppm concentration, a standard deviation of 200ppm (0.4% σ) is achievable.

We monitored the concentration of a dilute HF solution as it changed from 1500ppm to 8350ppm by spiking and then back to 1500ppm by dilution. Because of the short 1.5 sec analytical time of each measurement, several data points during mixing were captured in the graph (Fig. 4).

The effectiveness of the current method to measure solutions in transition can help fabs precisely control tank or point-of-use concentrations associated with process recipe changes.

Other cleaning solutions, including BEOL organic solvents, aqueous solutions, HF/H2SO4 blends, as well as many proprietary mixes were measured using the same setup, and similar accuracies as the previously mentioned examples were achieved.

Conclusion

A proven multivariate method measures ultra-dilute single- and multi-component wet processing solutions in real time. Simpler optical methods to measure concentrations often have problems associated with unavoidable small temperature variations. The multivariate method and system can be used to help control the concentrations of critical processing solutions in fabs, vital for necessary wafer cleaning without excessive material removal.

Authored by

Chenting Lin, Peter Bratin, Guang Liang, Michael Schneider, Eugene Shalyt
ECI Technology, Totowa, NJ

Reference

1. “Surface Preparation Technology Requirements: Near-term Years,” Table 68a, Front End Process, ITRS, 2005 Edition.
2. I.T. Jolliffe, Principal Components Analysis, 2nd ed., Springer, 2002.
3. E. Shalyt, et al., “Real-time Monitoring of Dilute Multicomponent Wet Processing Chemistries,” pp. 163-172, 25th SPWCC Proceedings, 2005.
4. Y. Shekel, et al., “Real-time Chemical Monitoring by NIR Spectroscopy,” pp. 245-250, Proceedings of the 208th ECS Meeting, Los Angles, CA, 2005.

Control of Electroless Nickel Baths

by jsmtgenesis

This paper reports the authors’ work on developing methods to analyze in-situ key parameters, including pH, nickel concentration and reducing agent concentration of electroless nickel baths. The purpose is to enable production line automated control of the deposition process. The analytical techniques developed within the scope of this work are discussed and their inclusion into an automated chemical monitoring system described. Thousands of data points have been collected to evaluate the system’ s performance. The corresponding results are presented in the context.

Introduction

As PCB manufactures comply with the requirements of lead-free regulations, alternative finishes such as ENIG (Electroless Nickel/Immersion Gold), immersion tin, and immersion silver have become widely adapted. Among those, ENIG provides a highly solderable surface that does not tarnish nor discolor – ensuring a relatively long storage time as compared to other alternative finishes. In addition, ENIG is known as an effective barrier preventing copper diffusion and maintaining solderability of the PCB pads.

One possible drawback of the ENIG finish is the probable nickel corrosion during immersion gold deposition – a defect commonly referred to as “black nickel” or “black pad” [1]. Black pads typically cause solderability failure and therefore need be avoided. The structure and the phosphorous content of the nickel layer are among the key factors in determining the subsequent formation of black pads. Those factors are, in turn, related to the composition and pH of the bath during electroless nickel deposition. Control of the electroless nickel bath is therefore key to defect-free ENIG processes. Bath properties change as solution ages, through consumption of components as well as creation of byproducts. To be able to control the bath, one must know the properties of the bath, and then make adjustment accordingly. Being able to analyze bath properties is thus the first step towards effective control of baths.

Unlike electroplating in which an external circuit provides the electrons to reduce metal ions into metal deposits, an electroless process must use reducing agents to provide the electrons. The most commonly used reducing agent in an electroless nickel bath is sodium hypophosphite. This paper reports the authors’ approaches to analyze divalent nickel (Ni2+) and sodium hypophosphite concentrations as well as pH in the bath.

Analytical Methods and Results

Analyses of pH and nickel concentration were conducted by a Quali-Stream inline chemical monitoring system (ECI Technology, Inc.), Figure 1.

Figure 01. Quali-Stream inline bath analyzer used in this work for controlling electroless nickel baths.

The system samples and analyzes solutions alternately from two production tanks based on pre-set schedules, and the solutions are automatically returned to the original production tanks after analysis. Solution inlets and outlets for multiple tanks are located on the left side panel of the system.

Analysis of nickel concentration is by spectroscopic method, based on Beer’s Law. Incoming light is partially absorbed by the solution under analysis. The higher the nickel concentration, the stronger the absorbance is, resulting in a weaker outgoing optical signal in the corresponding wavelength ranges. The outgoing light is collected by fiber optics and brought to an internal high-performance detector for analysis to ensure sensitivity and accuracy. A calibration curve is built by measuring the absorbance of solutions of known different nickel concentrations (carefully prepared ahead of time), Figure 2.

Figure 02. Calibration curve of nickel concentration

Absorbance of tank solution is measured and the corresponding nickel content is determined by mapping the absorbance to the calibration curve (an automatic process performed by software, while eliminating contribution from other species). Figure 3 shows the performance test results of the system measuring divalent nickel concentration.

Figure 03. Performance check of nickel concentration analysis

More than 4,000 data points were collected over a 3-day period, with the spectrometer automatically calibrating itself periodically. As can be seen from the figure, while analyzing the same standard solution of 6.0 g/l nickel concentration, the analytical method achieved very high accuracy – with the highest reading during the test period being of 6.094 g/l and the lowest 5.922 g/l. Statistical analysis of this data set further affirmed the high reliability of the method, with relative standard deviation at 0.86%.

Measurement of pH was conducted by a pH meter that has been built into the Quali- Stream analyzer. Figure 4 shows the long-term performance test of the system on measuring pH.

Figure 04. Performance check of pH analysis

More than 4,000 data points were taken at the same time as the aforementioned nickel concentration test was performed. The pH output reading had been maintained in a very narrow range throughout the 3-day period, with max at 4.727 pH unit, and min at 4.740 pH unit. Statistical analysis of this data set also showed a small relative standard deviation of only 0.06%. The accuracy of the system’s pH measurement was further affirmed by conducting an additional set of test. In this 2nd set of performance test, pH of one buffer solution was measured at several different temperatures. It’s been well documented that pH readings, even for the same solution, changes with the solution’s temperature. The pH vs. temperature results of this work, presented in figure 5 (blue data set), matched very closely with published data (pink data set), affirming the performance of the system.

Figure 05. Results of measuring pH buffer at multiple temperatures

Analysis of reducing agents was conducted by CVS (Cyclic Voltammetric Stripping) technique, the most widely adapted method to determine organic components concentrations in a copper electroplating bath [2]. The system used in this work to analyze sodium hypophosphite concentration was a Qualilab QL-5 plating bath analyzer (ECI Technology, Inc.). CVS technique applies a cyclic potential onto a platinum working-electrode that is immersed in the working solution (containing copper ions as well as precisely diluted bath sample from the process tank under analysis). The cyclic potential swings between pre-determined positive and negative limits. Copper is deposited onto the working electrode during the negative potential portion of the cycle and then completely stripped away during the positive potential portion of the cycle. The concentrations of additives in the solution affect the rate of copper plating onto the working electrode. When measuring reducing agents, the authors found that the deposition rate of copper in the working solution (note that Cu is the working metal used in the CVS analysis, though the reducing agent concentration in electroless nickel solution is being analyzed) increases monotonically with the increase of reducing agent concentrations.

Figure 6 illustrates the effect of hypophosphite concentration on voltammogram (I-V diagram monitoring the progress of CVS).

Figure 06. Effect of hypophosphite concentration (#1 < #2 < #3 < #4) on Voltammogram

Four carefully as-prepared test solutions of different hypophosphite concentrations gave distinct I-V curves during voltage scan. The enclosed areas under the curves, referred to as ‘peak area’ or Ar, correspond to the integration of current against the applied voltage and are therefore proportional to how fast plating/stripping occurred. A calibration curve, figure 7, plotting peak area vs. hypophosphite concentration can thus be built to compare with the peak area of an unknown solution and accordingly determine the hypophosphite concentration of the unknown solution.

Figure 07. Calibration curve of CVS peak area vs. hypophosphite concentration in the solution

Long-term statistics showed that using CVS to measure hypophosphite concentration could achieve better than 4% relative accuracy and 3.5% repeatability.

Summary and Conclusion

Methods for analyzing pH and nickel concentration in electroless nickel baths have been developed. Engineering efforts based on instrumentation know-how’s have integrated the developed methods into one automated system, enabling PCB production environments to analyze tank solutions in real time. The corresponding long-term results demonstrated both high accuracy and repeatability of the measurements. Closed-loop dosing based on the analytical results can ensure the stability of the electroless nickel bath and give production engineers full control of their parts quality. Additionally, reducing agent in the electroless nickel solution can be measured by CVS technique using a separate lab analyzer, although in this case sampling from the tank needs be performed manually.

The authors have established similar analytical approaches to analyze palladium activation solution, electroless copper solution and electroless cobalt solution, achieving comparable accuracies. Discussions of some of those topics have been published elsewhere [3].

Authored by:

Eugene Shalyt, Semyon Aleynik, Michael Pavlov, Peter Bratin, Chenting Lin
ECI Technology, Totowa, New Jersey, USA

Reference

1. George Milad and Jim Martin, “Electroless Nickel/Immersion Gold, Solderability and Solder Joint Reliability as Functions of Process Control,” CircuiTree, October 2000.
2. D. Tench and C. Ogden, “A New Voltammetric Stripping Method Applied to the Determination of the Brightener Concentration in Copper Pyrophosphate Plating Baths,” J. Electrochem. Soc. n. 125, p. 194 (1978).
3. P. Bratin, et. Al., “Development of Chemical Metrology for Electroless Deposition Baths,” ISTC Proceedings, March 2006.

Detection of Accelerator Breakdown Products in Copper Plating Baths

by jsmtgenesis

The mercaptopropylsulfonic acid (MPS) breakdown product of the bis(sodiumsulfopropyl)disulfide (SPS) additive used in acid copper plating baths accelerates copper electrodeposition and can be detected by cyclic voltammetric stripping (CVS) analysis. In the presence of oxygen, MPS decomposes rapidly in acid copper sulfate baths so that the CVS stripping peak area (Ar) decreases on successive cycles. The slope of a plot of Ar vs. log of the CVS cycle number (or time) provides a measure of the initial MPS concentration.

INTRODUCTION & BACKGROUND

Acid copper sulfate baths are employed in the “Damascene” process (1) to electrodeposit Cu within fine trenches and vias in dielectric material on semiconductor chips. Two organic additives are required to provide bottom-up filling of the Damascene features. The “suppressor” additive, which is typically high-molecular-weight polyalkene glycol (e.g., PEG), adsorbs strongly on the Cu cathode surface, in the presence of chloride ion, to form a film that sharply increases the overpotential for Cu deposition. The “anti-suppressor” or “accelerator” additive counters the suppressive effect of the suppressor to provide the accelerated deposition within trenches and vias needed for bottom up filling.

Close organic additive control needed for Damascene plating is provided by Cyclic Voltammetric Stripping (CVS) analysis, which involves alternate plating and stripping of Cu at a Pt rotating disk electrode. The additives are detected from the effect that they exert on the electrodeposition rate measured via the Cu stripping peak area (Ar). The accelerator concentration is typically determined by the linear approximation technique (LAT) or modified linear approximation technique (MLAT) described by Bratin (2). During Damascene Cu plating, however, additive species break down into breakdown products that may interfere with the electrodeposition process. These breakdown products need to be controlled to ensure that high quality Damascene deposits are obtained. A method for detecting suppressor breakdown products was described in our earlier publications (3,4).

This paper describes a CVS method for detecting breakdown products of accelerator additives that are widely used for Damascene copper plating. Results are presented for the 3-mercaptopropylsulfonic acid (MPS) species, which is a breakdown product of the bis(sodiumsulfopropyl)disulfide (SPS) additive (5).

EXPERIMENTAL DETAILS

CVS measurements were made using a Qualilab QL-10® plating bath analyzer (ECI Technology, Inc.) with a polyethylene beaker cell containing 50 mL of solution (open to the atmosphere). For some experiments to verify that oxygen plays a role in MSA decomposition, the cell was partially sealed and deaerated via nitrogen bubbling (stopped during the CVS measurements). The supporting electrolyte contained 40 g/L Cu (added as CuSO4 . 5 H2O), 10 g/L H2SO4, 50 ppm chloride ion, and 2.0 g/L of 5000 molecular weight (MW) polyethylene glycol (Aldrich). The SPS and MPS materials were purchased from Raschig Chemical (Germany). The working electrode was a Pt rotating disk (4 mm diameter, 2500 rpm). Unless otherwise noted, the potential was scanned at 100 mV/s between a positive limit of +1.575 V and a negative limit of either -0.225 and -0.325 V vs. SSCE-M (standard silver-silver chloride electrode modified by replacing the solution with a saturated AgCl solution also containing 0.1 M KCl and 10 volume% sulfuric acid). The counter electrode was usually a stainless steel rod (6 mm diameter). During CVS measurements, the solution temperature was controlled at 24°C within +0.5°C. Specimens of MPS and SPS were injected into the cell at the positive limit in the CVS cycle. The effects of the commercial Viaform™ (Enthone Inc.) and Ultrafill™ (Shipley, Inc.) additives (at normal concentrations) were also investigated.

RESULTS & DISCUSSION

MPS Analysis Method

Figure 1 shows that Ar measured on the first CVS cycle after addition of MPS to the acid copper electrolyte varies monotonically with the MPS concentration. However, a simple Ar measurement cannot be used for MPS analysis since organic additives and other species present in plating baths also affect Ar values.

Fig. 1 Plot of Ar for first CVS cycle as a function of initial MPS concentration in acid copper electrolyte (-0.225 V limit).
Fig. 2 Plots of Ar as a function of CVS cycle number for acid copper supporting electrolyte containing SPS or various concentrations of MPS (–0.225 V limit).

Figure 2 shows that Ar remains constant for acid copper baths containing only SPS, but decreases monotonically with potential cycling in the presence of the MPS breakdown product. Both compounds tend to accelerate the copper deposition rate but the accelerating effect of MPS is stronger and much more time-dependent. After addition of the Viaform™ and Ultrafill™ accelerator additives at the normal concentrations, the Ar values were also constant (not shown) but were smaller (1.5 and 1.4, respectively) than the value of 2.2 observed for SPS at 1.0 ppm concentration. For the potential scan rate and limits used, a CVS cycle corresponded to 38 seconds and copper deposition occurred over a time frame of about 6 seconds. Since the MPS and SPS specimens were injected at the positive potential limit, the copper deposition rate measurement for cycle number 1 began after about 16 seconds and ended at about 22 seconds (onset for copper deposition is about 0.0 V vs SSCE-M). It is clear from these data that MPS decomposes rapidly when its concentration is high, and much more slowly as its concentration decreases.

Figures 3 and 4 illustrate the effects of delaying CVS cycling (after addition of the MPS sample) and interrupting CVS cycling.

Fig. 3 Effects of delays and interruptions in CVS cycling on plots of Ar as a function of CVS cycle number for acid copper supporting electrolyte containing 1.0 ppm MPS (–0.325 V limit).
Fig. 4 Plots of Ar vs. CVS cycle number for which a 3-minute delay was taken into account by shifting the first data point to the 5th cycle (conditions same as Fig. 3).

Note that a relatively negative potential scan limit (-0.325 V) was used to enhance measurement sensitivity. It is evident that Ar continues to decrease unabated even when no voltage is applied to the working and counter electrodes, indicating that MPS decomposes chemically in the bath. When the beginning of the curve for a 3-minute delay was shifted to the 5th CVS cycle (corresponding to 3.2 minutes), the 3-minute delay curve practically coincided with the curve for which cycling had been interrupted for 2 cycles (Fig. 4). Potential cycling appears to actually slow MPS decomposition, possibly because of SPS formation.

Figure 5 illustrates that an exposed copper counter electrode tends to increase the rate of MPS decomposition compared to a stainless steel electrode (or a copper electrode partitioned from the electrolyte via a double-junction glass frit). This effect is relatively small and may result from adsorption of MPS on the relatively large copper counter electrode.

Fig. 5 Effects of counter electrode on plots of Ar as a function of CVS cycle number for acid copper supporting electrolyte containing 1.0 ppm MPS (–0.325 V limit).

Figure 6 shows that the decrease in Ar after MPS additions is exponential since a plot of Ar vs. Log (CVS cycle number) is linear.

Fig. 6 Plot of Ar vs. Log (CVS cycle number) as a function of initial MPS concentration for acid copper supporting electrolyte containing 1.0 ppm MPS (–0.225 V limit).

The theoretical basis for this empirical relationship would be difficult to ascertain from the present data since the measured copper deposition rate is a composite for a range of potentials, and both electrochemical and chemical processes may be involved in the decomposition process. Nonetheless, as shown in Fig. 7, the slope of such plots provides a measure of the initial MPS concentration.

Fig. 8 Effect of SPS concentration on Ar vs. CVS cycle number curve for acid copper supporting electrolyte with 1.0 ppm MPS added.
Fig. 9 Effect of SPS concentration on Ar vs. Log (CVS cycle number) curve for acid copper supporting electrolyte with 1.0 ppm MPS added.

Figure 10 illustrates the effect of negative potential scan limit on plots of Ar vs. Log (CVS cycle number). Linear plots are obtained in all cases, although the slopes vary. Figure 11 shows the dependence of the slope on the negative potential limit. Obviously, the negative potential limit must be held constant for the MPS analysis.

Fig. 10 Plot of Ar vs. Log (CVS cycle number) as a function of negative potential limit for acid copper supporting electrolyte containing 1.0 ppm MPS.
Fig. 11 Plot of the slopes from Fig. 10 as a function of CVS negative potential limit.

Previous work by Healy et al. (6) has shown that SPS and MPS undergo complicated chemical and electrochemical reactions in acid copper plating baths. The SPS species, also known as 4,5-dithiaoctane-1,8-disulphonic acid, is slowly oxidized chemically in the bath but only in the presence of copper metal, although the oxidation rate is accelerated in the presence of oxygen. A complex involving Cu(I) and MPS, e.g., Cu(I)SCH2CH2CH2SO3H, apparently plays a key role as an intermediate in electrochemical reduction of SPS and oxidation of both SPS and MPS in acid copper baths. Nonetheless, Healy et al. conclude that oxidation of MPS via this intermediate does not lead to regeneration of the SPS species. However, Moffat et al. (5) provide convincing evidence that decomposition of MPS eventually results in formation of SPS in acid copper baths.

Figure 12 shows that the CVS stripping peak area (Ar) for a 1.0 ppm MPS acid copper electrolyte decays (after about two days) to a constant value corresponding to that for a 1.0 ppm SPS electrolyte.

Fig. 12. Effect of aging on the Ar values for a 1.0 ppm MPS acid copper electrolyte compared to the constant Ar value for a 1.0 ppm SPS electrolyte.

Since the concentration of the two species based on weight was the same (1.0 ppm), the molar concentration of the MPS electrolyte was initially double that of the SPS electrolyte. The equivalent Ar values observed for the aged MPS electrolyte and the fresh SPS electrolyte indicate that the MPS dimerized to SPS, resulting in the same molar concentration of SPS in both solutions. Thus, our results support the conclusion of Moffat et al. (5) that MPS dimerizes to form SPS in acid copper baths. Our results also indicate that this process is reversible (under some conditions) since the initial Ar value for the MPS solution aged for one day was somewhat greater than the final Ar value from the previous day (fresh MPS solution).

Oxygen also plays a role in the decomposition of MPS in acid copper baths since partial deaeration of the solution was found to significantly reduce the decomposition rate (slow the decrease in Ar value with time). On the other hand, stirring of the solution had little effect, indicating that mass transport is not an important factor. Likewise, removal of the Pt rotating disk electrode from the solution had no effect. Future studies will determine the effects of acidity and copper ion concentration. The goal of this work is to provide metrology that helps tool manufacturers, chemical suppliers, and users to better control acid copper plating processes.

CONCLUSIONS

The mercaptopropylsulfonic acid (MPS) breakdown product of the bis(sodiumsulfopropyl)disulfide (SPS) additive used in acid copper plating baths can be detected by cyclic voltammetric stripping (CVS) analysis. Decomposition of MPS in acid copper baths apparently involves dimerization to SPS, which is accelerated in the presence of oxygen.

Authored By

M. Pavlov, E. Shalyt, P. Bratin and D. M. Tench
ECI Technology, Inc.
60 Gordon Drive, Totowa, NJ 07512

REFERENCES

1. P. C. Andricacos, Electrochem. Soc. Interface, p. 32, Spring 1999.
2. P. Bratin, Proc. AES Analytical Methods Symp., Chicago, IL (March 1985)
3. P. Bratin, G. Chalyt, A. Kogan, M. Pavlov, J. Perpich and M. Tench, “Detection of Suppressor Breakdown Contaminants in Copper Plating Baths”, 203rd ECS Meeting, Paris, France (Apr. 27 – May 2, 2003)
4. M. Pavlov, E. Shalyt and P. Bratin, Solid State Tech. 46(3), 57 (2003)
5. T. P. Moffat, B. Baker, D. Wheeler and D. Josell, Electrochem. Solid State Lett. 6(4), C59 (2003)
6. J. P. Healy, D. Pletcher and M. Goodenough, J. Electroanal. Chem. 338, 167 (1992)

Control of Tin/Lead Solutions for Electrodeposition of Bumps

by jsmtgenesis

Ingredients of commercial SolderOn SC and SolderOn BP chemistry as well as samples of aged solutions were provided by Shipley Company. Reagents used were from Aldrich.

Abstract

Typical bumping process includes formation of bumps through UBM copper electrodeposition, followed by deposition of tin/lead coating. Quality control of the tin or tin/lead electroplating solutions is critical to meet demands on the properties of the plated deposit, cost, and environmental issues. Even though lead is being phased-out as enemy of environment and many replacements are being tested, it is still widely used. As all electrochemical processes, tin/lead plating is a dynamic system; unless precisely controlled, concentration of consumable components and breakdown products soon gets outside of acceptable range resulting in manufacturing problems and eventually rejects.

Copper Electrodeposition process for bumping is similar and somewhat less challenging than Damascene copper electrodeposition process used for the interconnects. The control of the Damascene copper process has been a focus of research by all major semiconductor companies in recent years. We have previously demonstrated an on-line controller for complete analysis of copper electroplating solutions1,2. This paper will focus on automated on-line determination of tin/lead coatings, by reviewing analysis of up to 6 components in a commercial tin/lead electrodeposition bath. While the benchtop analysis of tin/lead solutions has been utilized for a number of years, many obstacles exist when transitioning such procedures into automated on-line system due to steps such as gravimetric or extraction procedures.

While determination of metals and acid is reasonably common, measurement of proprietary organic additives remains a challenging task. Organic additives are the key ingredients in the plating solution that influence the properties and quality of the deposits. Cyclic Voltammetric Stripping (CVS) method is an established analytical technique that has long been demonstrated to be applicable to analysis of tin and tin/lead additives in various plating solutions (fluoroborate, sulfate, MSA, and PSA). Detailed description was published in an earlier paper3. This paper will describe an on-line analysis of all components generally used in a tin/lead bumping process. The total automated analysis takes about 40-60 minutes with accuracy better than 10% and reproducibility better than 5%.

Experimental

Experiments were performed using Qualilab QL-10® CVS/Titration benchtop analyzer from ECI technology, Qualiline QLC-7500® on-line analyzer from ECI technology, S2000 Spectrometer from Ocean Optics. Titration was performed using pH and ORP combination electrodes from Thermo Orion. Analysis of three inorganic components (Methanesulfonic acid – H-MSA, Tin (II), and Lead) and three organic components (Primary additive, Secondary additive, and anti-oxidant) was tested and validated using both, benchtop and on-line, analyzers.

A. Analysis of Inorganic components

1. Methanesulfonic Acid (H-MSA)

As for many acids, an acid-base titration of H-MSA with NaOH is a recommended procedure. Manual analysis typically employs a color indicator; use of pH electrode to follow the titration allows for easier automation of this analysis, as well as improved accuracy and reproducibility.

Regardless whether color indicator or pH electrode is employed, titration with NaOH measures “total” acid, which is a sum of tin and H-MSA concentrations, because tin hydroxide formation is at pH values too close to the acid-base titration end-point, making it difficult to separate these two species. To measure “Free” H-MSA, one recommended procedure3 is to add masking agent, such as magnesium EDTA, which ties tin from forming hydroxide or shifts hydroxide formation away from the acid-base endpoint. This allows measurement of free H-MSA. Figure 1 shows titration curve for the free acid.

Figure 1. Determination of Free Acid

2. Determination of Sn (II)

There are two approaches that we have tested for analysis of tin (II) in the tin/lead plating solutions: (a) iodometric titration in acidic solution using starch (manual) or ORP electrode (on-line) as end-point indicators and (b) acid-base titration with NaOH with and without Mg-EDTA masking agent and subsequent extraction of tin from the two experiments.

The acid-base titration with NaOH requires two experiments; first titration of the solution under test with NaOH, where endpoint is proportional to the sum of concentrations of tin (II) and free acid (“total acid value”), and second experiment where Mg-EDTA is added prior to titration with NaOH, resulting end-point volume being proportional to concentration of free acid only. The difference of the two end volumes then yields the concentration of tin (II). This approach yields results with accuracy of 10% and reproducibility of 5%. This method, however, looses accuracy and precision with “high lead/ low tin” formulations, when tin concentration falls below 10 g/l. At these low tin levels the difference between total and free acid is small, and falls within the experimental error for determination of end-point.

The industry recommended determination of Sn (II) is based on its oxidation by iodine in acidic media with starch as color indicator for detection of equivalence point. This method is employed with most benchtop units. Our on-line approach utilizes ORP electrode to automate the equivalence point detection without need for any color indicator. Figure 2 shows curves obtained with this method.

Figure 2. Determination of Tin

Experimental data indicates that “iodometric” titration yields better accuracy and reproducibility than set of acid-base titrations, while the latter has an advantage of simpler analysis, less electrodes, less reagents, improved reliability, and thus lower cost and cost of ownership.

3. Determination of Pb

Two approaches are described in literature:

  • Determination by AAS
  • Oxidation of Sn(II) followed by titration with EDTA

Both procedures contain multiple steps, which are very difficult, if not impossible, to automate.

The technique implemented on the on-line unit is a second step in the analysis of total acid, which is the approach used for analysis of Copper in the Damascene systems. After titration of the solution under test with NaOH to determine total acid (acid + tin), excess of EDTA is added. EDTA reacts with lead releasing additional acid proportional to amount of lead in tested solution, which is then further titrated by NaOH using pH electrode as an indicator. The second equivalence point is then proportional to concentration of lead. Resulting curve is shown in Figure 3.

Figure 3. Determination of Total Acid and Lead

For higher tin concentrations, complete set of 4 analyses (free acid, total acid, tin, and lead) can be determined using set of two titrations with but single electrode (pH) and 3 reagents: Magnesium EDTA, Sodium EDTA, and NaOH. Needless to say, pH electrode is the most robust ion selective electrode, thus yielding highly reliable methods.

B. Analysis of Organic components (additives)

1. Determination of Primary Additive

Primary additive is the key component of the Tin/Lead electrodeposition solution, responsible for grain size and other properties of deposit. It is Primary suppressor and is typically monitored by CVS (Cyclic Voltammetric Stripping) technique. In this technique, activity of the primary additive is measured from its effect on the plating rate of the metal, in this case tin or tin/lead. CVS method employed for the Primary additive is Dilution Titration, where the Supporting electrolyte is “titrated” with solution under test, causing suppression (reduction of plating rate). Titration curve of unknown solution is then compared to that of calibration standard. Details of this method as well as its application to the analysis of the Primary Additive in Tin/Lead systems have been previously described3,5. Figure 4 shows curves obtained for 100mL/L of the Primary Additive with different levels of the Secondary Additive component.

Figure 4. Determination of Primary

There is a wide variety of commercially available Sn/Pb formulations. All additives tested to date display different degrees of the same suppression behavior, and can therefore be monitored using the same technique. The very same technique is used for analysis of suppressors in the copper electroplating processes.

2. Determination of Secondary Additive

Secondary Additive component provides “Secondary” suppression. The suppression effect of this component is much weaker than that of Primary additive. At standard conditions, effect of Secondary component can be seen, but is not quantitatively analyzable due to variation of other components in the solution, even if they are within process spec.

Secondary additive contains dye and most chemical suppliers recommend analysis using a photometric method with extraction, in order to avoid interferences from other ingredients. Extraction technique can be tricky, and is hardly viable for automation.

We have equipped the analyzer with spectrophotometric capability and optimized experimental conditions in order to provide on-line photometric analysis for the Secondary additive component without need for extraction. Figure 5 shows spectral curves obtained for low, target, and high levels of Secondary additive, as well as effect (or better non-effect) of other solution components. Calibration curve is shown in Figure 6.

Figure 5. Spectrometric determination of Secondary
Figure 6. Determination of Secondary

Results with standard and aged solutions indicate that on-line technique without extraction gives results that are consistently 5-10% higher than benchtop extraction technique.

3. Determination of “Active” Secondary Additive

During the electrodeposition additive undergoes multiple transformations, forming a variety of breakdown products. The photometric methods measure analytical concentration of a compound or a group of compounds that absorb in the same frequency region, thus they might or might not include effect of the breakdown products. Since Secondary additive affects plating through its suppression of the electroplating process rather than color, analysis of secondary by photometry might not be the most appropriate analytical method. CVS technique, on the other hand, is an electroanalytical procedure, which monitors concentration of “active” Secondary additive, or its activity.

As mentioned above, the suppression effect of Secondary Additive is quite weak at standard parameters. By optimization of parameters and use of Pulse (CPVS) technique, sensitivity of analysis can be significantly improved. Figure 7 shows response curve for the Secondary Additive utilizing the optimized conditions.

Figure 7. Determination of Active Secondary

Results obtained for various chemistries indicate that in aged solutions, the activity of the Secondary Additive as determined by CVS is about 20% lower than total Secondary Additive concentration as obtained with photometric methods, with activity decreasing with increasing bath age.

During these study, we also compared results of Photometric and Electrochemical analysis obtained from standard solutions, which did not go through electro-plating process. The results obtained for Secondary additive were the same, which confirms the ability of electrochemical (CVS) technique to monitor its “active” concentration. This approach, when two independent techniques are used for analysis of the plating bath, allows to characterize the conditions of plating solutions and its transformations during electroplating process.

4. Determination of Anti-oxidant

Recommended analysis of Antioxidant is performed by photometric analysis after extraction and precipitation. Needless to say, such procedures, similar to the previously mentioned procedure for the Secondary Additive, are very difficult, if not impossible, to automate. We have optimized the analytical methods to allow use of spectrophotometric measurement without need for extraction and precipitation steps, thus allowing us to automate the analysis. Figure 8 shows spectral responses for low, target, and high concentrations of anti-oxidant and effect of other solution components. Figure 9 shows linearity of the response.

Figure 8. Determination of Antioxidant
Figure 9. Linearity of Antioxidant Response

C. Validation of Results

All analytical procedures described above were validated by:

  • repeated analysis of standard solutions with various ratios of components to establish parameters, reproducibility and accuracy
  • repeated analysis of aged sample to establish reproducibility
  • recovery of known dilution of aged sample to establish accuracy and reproducibility
  • recovery of known spike with component of interest into aged sample to establish that we are measuring component of interest, as well as accuracy
  • effect of spike with other components in the matrix into aged sample to evaluate interference from these components
  • correlation of results to recommended benchtop methods

The results obtained for standard solutions and recovery of spike and dilution were within 10% of expected values, as were correlations with recommended benchtop methods.

D. On-Line Analysis

All of the above mentioned analyses were tested individually in the on-line analyzer. Analyzer employs 2 cells; one cell is used for inorganic titrations and second cell for analysis of organic additives by CVS and UV-VIS Photometry. Standard qualification included 10 data points for each of 3 compositions: Target, Low end of process window, and High end of process window. Accuracy in all experiments was within 10% and relative standard deviation within 5%. Cycle time for complete analysis is 40 min (without CVS measurement for activity of Secondary Additive) or 60 min with activity of Secondary Additive. Picture of the online analyzer is shown in Figure 10.

Figure 10. On-line Analyzer for Sn/Pb Electrodeposition Process

Authored By

P. Bratin, E. Shalyt, M. Pavlov, J. Berkmans
ECI Technology, 60 Gordon Drive, Totowa, NJ 07512

References

1. P. Bratin, E. Shalyt, M. Pavlov. “Automated On-line Control of Plating Bath Additives Increases Wafer Yield”, Semiconductor Fabtech –14th Edition, pp. 205-207
2. M. Pavlov, E. Shalyt, P. Bratin. “A New Generation of CVS Monitors Cu Damascene Plating Bath”, Solid State Technology, March 2003, vol. 46, N 3, pp. 57-60
3. P. Bratin, E. Shalyt, M. Pavlov, and R.Gluzman “Control of Tin and Tin/Lead Electroplating Solutions” Proc. IPC’96 , pp S16 1-14
4. Atotech Technical Spec Data Sheets for Sulfotech-M process.
5. P. Bratin “New developments in use of CVS for analysis of plating solutions”, Proc. AES Analytical Methods Symposium, March 1985
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