• No results found

CHAPTER 5 CONCLUSIONS AND FUTURE WORK

A.3 Model for Membrane Acoustics

In this section we develop a model to obtain the frequency response in a pump-probe setup if the transient response from a single pump pulse is a signal (S0(t)). We develop the final in-phase and out-of phase expressions

for the voltages read by the lock-in amplifier which are indendent of the specific character of the single pulse resposne S0(t). The method developed

herein is generic and may be applied to electronic, thermal and acoustic measurements. Figure A.2 provides an overview of the measurement. A series of pump pulses modulated by an envelope function trigger a response S0(t) that is the cumulative contributions of all pulses. The pump and probe

as follows Lpump(t) = EL TP ∞ X n=−∞ rect [θL] where θL = t − (αL+ nTL) TP Lprobe(t) = EP TP ∞ X n=−∞ rect [θP] where θP = t − (αL+ nTL+ td) TP . (A.9) td is the delay time between pump and probe pulses. EL and EP are the

energies per pulse for pump and probe beams respectively. I the limit of short pulses, we can approximate the pulsed by delta functions as

Lpump(t) ∼ EL ∞ X n=−∞ δ [t − (αL+ nTL)] Lprobe(t) ∼ EP ∞ X n=−∞ δ [t − (αL+ nTL+ td)]. (A.10)

The modulation envelope for a square wave modulation becomes M (t) = M0+ ∆M (t) M (t) = M0+ ∆M0 ∞ X n=−∞ rect [θM] where θM = t − (αM + nTM) pTM M (t) = ( M0+ ∆M (t) −pT2M + αM + nTM < t < pT2M + αM + nTM M0 else (A.11) The total response signal form a series of pump pulses becomes

S (t) = S0(t) ∗ (Lpump(t) × M (t)) . (A.12)

The reflectance is then

R (t) = R0+ ∆R0(t) + ∆R (t) R (t) = R0+ dR dS × S0(t) ∗ (Lpump(t) × M0) + dR dS × S0(t) ∗ (Lpump(t) × ∆M (t)) (A.13)

dR/dS is the change in reflectance with respect to the measured signal. The reflected intensity is therefore

I (t) = R (t) × Lprobe(t) I (t) = R0 × Lprobe(t) + dR dS × [S0(t) ∗ (Lpump(t) × M0)] × Lprobe(t) + dR dS × [S0(t) ∗ (Lpump(t) × ∆M (t))] × Lprobe(t) I (t) = I0(t) + ∆I0(t) + ∆I (t) (A.14) In frequency domain this becomes

˜ I (f ) = ˜I0(f ) + ∆ ˜I0(f ) + ∆ ˜I (f ) ˜ I (f ) = R0 × ˜Lprobe(f ) + dR dS × h ˜S0(f ) × ˜Lpump(f ) × M0i ∗ ˜Lprobe(f ) +dR dS × h ˜S0(f ) × ˜Lpump(f ) ∗ ∆ ˜M (f )i ∗ ˜Lprobe(f ) (A.15) The lock-in amplifier measures the rms voltage of the

Vrms(t) =

Q (f ) √

2 × RP (λ) × I (t) . (A.16) RP(λ) is the responsivity of the photodetector at the wavelength λ of the

reflected probe beam. Q (f ) is the effective quality factor of the electron- ics connecting the photodetector to the lock-in amplifier at the modulation frequency fM of the pump beam. Finally, the in-phase and out-of-phase volt-

the probe are Vin(fM) = Q (fM) √ 2 × RP(λ) × 1 2 dR dS × p∆M0× ELEP T2 L × sinc (p) × ∞ X k=−∞    ˜ S0  k TL + 1 TM  exphi2π td TM i + ˜S0  k TL − 1 TM  exph−i2π td TM i    exp  i2πktd TL  Vout(fM) = Q (fM) √ 2 × RP(λ) × 1 2 dR dS × p∆M0× ELEP T2 L × sinc (p) × ∞ X k=−∞    ˜ S0  k TL + 1 TM  exphi2π td TM i − ˜S0  k TL − 1 TM  exph−i2π td TM i    exp  i2πktd TL  (A.17) This concludes our derivation of the frequency response. We have used this model to simulate the acoustic response at negative delay times for long-lived acoustic modes. We assumed a single pulse acoustic response signal of the form S0(t) = A0exp  −t − tac τ  sin [2πf0(t − tac)] (A.18)

Our fits of the negative delay time data yielded damping times that are similar to the ones obtained through linear regression.

APPENDIX B

SUPPLEMENTARY INFORMATION FOR

CHAPTER 2

B.1

Instrumentation and Uncertainties in Thermal

Conductivity Measurements

A DC current, Idc ∼ 0-3 µA from a constant current source (Keithley 6221)

provided the heating power. AC currents, Iac ∼ 150 250 nA from two

different lock-in amplifiers (SR830) measured coil resistances. Two addi- tional lock-in amplifiers that were phase-locked to the previous two provided voltages across the heating and sensing coils respectively. A fifth lock-in measured the voltage across one of the supporting legs and the heating coil in order to determine the heating power supplied to the device. We used the of f set and expand functions on the SR830 to decrease truncation and digitization errors in our measurement. These allow measurements of coil resistances with 0.5 µΩ resolution.

Thermal fluctuations outside the cryostat are a major source of noise in measuring the change in coil resistance, R. The voltage source of the lock-in amplifier, combined with a high precision resistor (20 MΩ) provided the con- stant ac current in our measurement. Both suffer from amplitude instability due to thermal fluctuations. The resulting ac current has an amplitude drift of around 70 ppm/oC. This instability in the ac current induces a random noise in the resistance measurement. Its standard deviation is around 250 mΩ that corresponds to the lower limit of measurable change in resistance. While thermal fluctuations affect the measurement of change in resistance R, other sources of noise affect the measurement of the zero current resistance, R0 (R at Idc = 0) at each temperature point T0. Pressure fluctuations in the

helium Dewar, during the measurement, bring the entire chip out of thermal equilibrium and induce fluctuations in R0. We installed a low-pressure valve

(1 psi) on the venting line of the Dewar in order to reduce this effect. Another source of error in measuring R0 relates to the long temperature transients of

the measurement. The suspended platforms are well insulated from the chip and this leads to a settling time of ∼ 4 hours at low bath temperatures (T0

∼ 5 K). This settling time increases with temperature, reaching around 10 hours at 300 K. These two factors contribute an uncertainty of 1-2 Ω in R0.

Finally, uncertainty in measuring the diameters and lengths of the nanowires through a combination of SEM and TEM is 10%.

B.2

Supplementary Figures

Figure B.1: SEM micrographs of silicon nanowires MAC-etched with ρ =75 % and different M (H2O) . We can reduce the etch rate independently of

rho with water dilution. The silicon nanowires remain straight and sharp all the way to the tips as the etch rate reduces by three orders of magnitude.

Figure B.2: An SEM image of the measurement platform showing a suspended nanowire between the heating and cooling islands. The inset shows a high resolution image of the suspended nanowire.

Figure B.3: Height profile (TEM trace), autocorrelation function and Fourier power spectrum for a nanowire that is 100 nm in diameter and has an RMS height σ = 1.0 nm and a correlation length lc = 4 nm. For

comparison, we show the height profile, autocorrelation function and Fourier power spectrum of random Gaussian and exponential profiles having the same RMS height and correlation length. To generate the random profiles, we used a moving average numerical technique described in [7]. a is the lattice constant of Silicon.

Figure B.4: Temperature dependence of thermal conductivity for single nanowires with different diameters φ, RMS roughness heights σ and correlation length lc. The exponent n is the temperature dependence of κ

as obtained from the fits to the low temperature data using a modified Holland-Callaway model. Roughness and thermal conductivity data is from [38].

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