1. A complementary metal-oxide-semiconductor (\u201cCMOS\u201d) backside illuminated imaging sensor, comprising:
a semiconductor layer having a P-type region;
a frontside P+ doped layer formed within the semiconductor layer;
a backside P+ doped layer formed within the semiconductor layer;
an imaging pixel having a photodiode formed within the semiconductor layer, wherein the photodiode is an N\u2212 region formed within the P-type region of the semiconductor layer between the frontside P+ doped layer and the backside P+ layer.
2. The backside illuminated imaging sensor of claim 1, wherein the backside P+ doped layer is a boron doped layer.
3. The backside illuminated imaging sensor of claim 2, wherein the boron doped layer includes boron fluoride (BF2).
4. The backside illuminated imaging sensor of claim 2, wherein the boron doped layer includes boron derived from a diborane (B2H6) source.
5. The backside illuminated imaging sensor of claim 2, wherein the boron doped layer has an ion concentration selected to increase a quantum efficiency of the imaging pixel.
6. The backside illuminated imaging sensor of claim 2, wherein the boron doped layer has an ion concentration selected to reduce one or more of dark current and white pixel defects.
7. The backside illuminated imaging sensor of claim 6, wherein the boron doped layer has a first ion concentration at a back surface of the semiconductor layer and has a second ion concentration at a distance from the back surface of the semiconductor layer, wherein the first ion concentration is greater than the second ion concentration.
8. The backside illuminated imaging sensor of claim 7, wherein the first ion concentration is in the range of approximately 1\xd71017 ionscm3 to approximately 1\xd71020 ionscm3.
9. The backside illuminated imaging sensor of claim 7, wherein the second ion concentration is in the range of approximately 1\xd71014 ionscm3 to approximately 2\xd71015 ionscm3.
10. The backside illuminated imaging sensor of claim 9, wherein the distance between the second ion concentration and the back surface of the semiconductor layer is in the range of approximately 50 nm to approximately 400 nm.
11. The backside illuminated imaging sensor of claim 9, wherein the distance between the second ion concentration and the back surface of the semiconductor layer is in the range of approximately 80 nm to approximately 200 nm.
12. The backside illuminated imaging sensor of claim 7, wherein a distance between the back surface and a front surface of the semiconductor layer is selected to increase a quantum efficiency of the imaging pixel.
13. The backside illuminated imaging sensor of claim 12, wherein the imaging pixel is a 1.75 micron CMOS backside illuminated imaging pixel and the distance between the back surface and the front surface of the semiconductor layer is in the range of approximately 2 microns to approximately 4 microns.
14. The backside illuminated imaging sensor of claim 12, wherein the imaging pixel is a 1.75 micron CMOS backside illuminated imaging pixel and the distance between the back surface and the front surface of the semiconductor layer is in the range of approximately 2.3 microns to approximately 3.5 microns.
15. The backside illuminated imaging sensor of claim 12, wherein the imaging pixel is a 1.4 micron CMOS backside illuminated imaging pixel and the distance between the back surface and the front surface of the semiconductor layer is in the range of approximately 1.5 microns to approximately 3.0 microns.
16. The backside illuminated imaging sensor of claim 12, wherein the imaging pixel is a 1.4 micron CMOS backside illuminated imaging pixel and the distance between the back surface and the front surface of the semiconductor layer is in the range of approximately 1.9 microns to approximately 2.7 microns.
17. The backside illuminated imaging sensor of claim 1, wherein the imaging pixel further includes:
a floating diffusion region within the semiconductor layer proximate to a front surface of the semiconductor layer; and
a transfer gate coupled between the photodiode and the floating diffusion.
18. The backside illuminated imaging sensor of claim 1, further comprising:
a micro-lens disposed on a back surface of the semiconductor layer below the backside P+ doped layer and optically aligned to focus light through the back surface of the semiconductor layer, through the backside P+ doped layer and onto the photodiode; and
a color filter disposed between the micro-lens and the photodiode to filter the light.
19. The backside illuminated imaging sensor of claim 18, further comprising an anti-reflective coating disposed between the color filter and the back surface of the semiconductor layer to substantially prevent reflection of the light.
20. A method, comprising:
receiving an optical signal at a back surface of a semiconductor layer;
transmitting the optical signal through a backside P+ doped layer formed between the back surface of the semiconductor layer and a photodiode of an imaging pixel formed within the semiconductor layer, wherein the photodiode is an N\u2212 region formed within a P-type region of the semiconductor layer;
transmitting the optical signal onto the photodiode;
pinning the photodiode with a frontside P+ doped layer formed between a front surface of the semiconductor layer and the photodiode; and
generating electrical signals responsive to the optical signal with the photodiode.
21. The method of claim 20, wherein the backside P+ doped layer is a boron doped layer.
22. The method of claim 21, wherein the boron doped layer has a first ion concentration at the back surface of the semiconductor layer and has a second ion concentration at a distance from the back surface of the semiconductor layer, wherein the first ion concentration is greater than the second ion concentration.
23. The method of claim 20, wherein the imaging pixel is a complementary metal-oxide-semiconductor (\u201cCMOS\u201d) backside illuminated imaging pixel.
24. An imaging sensor comprising:
a semiconductor layer having a front surface and a back surface, the semiconductor layer having a complementary metal-oxide-semiconductor (\u201cCMOS\u201d) backside illuminated array of imaging pixels, wherein each imaging pixel includes:
a P-type region;
a frontside P+ doped layer formed within the semiconductor layer;
a backside P+ doped layer formed within the semiconductor layer; and
a photodiode formed within the semiconductor layer, wherein the photodiode is an N\u2212 region formed within the P-type region between the frontside P+ doped layer and the backside P+ layer.
25. The imaging sensor of claim 24, wherein the backside P+ doped layer is a boron doped layer.
26. The imaging sensor of claim 25, wherein the boron doped layer has a first ion concentration at the back surface of the semiconductor layer and has a second ion concentration at a distance from the back surface of the semiconductor layer, wherein the first ion concentration is greater than the second ion concentration.
27. The imaging sensor of claim 24, wherein each imaging pixel further includes:
a floating diffusion region formed within the semiconductor layer; and
a transfer gate coupled between the photodiode and the floating diffusion region.
28. A method of fabricating an array of complementary metal-oxide-semiconductor (\u201cCMOS\u201d) backside illuminated imaging pixels, comprising:
fabricating a plurality of photodiodes within a semiconductor layer from a front surface of the semiconductor layer, wherein the plurality of photodiodes are N\u2212 regions formed within a P-type region;
implanting a frontside P+ doped layer within the semiconductor layer from the front surface of the semiconductor layer; and
implanting a backside P+ doped layer within the semiconductor layer from a back surface of the semiconductor layer, wherein the N\u2212 region is disposed between the frontside P+ doped layer and the backside P+ doped layer.
29. The method of claim 28, further comprising laser annealing the backside P+ doped layer from the back surface of the semiconductor layer.
30. The method of claim 28, wherein the backside P+ doped layer is a boron doped layer.
31. The method of claim 30, wherein the boron doped layer has a first ion concentration at the back surface of the semiconductor layer and has a second ion concentration at a distance from the back surface of the semiconductor layer, wherein the first ion concentration is greater than the second ion concentration.
32. The method of claim 31, wherein a slope of ion concentrations between the first ion concentration and the second ion concentration is selected to increase a quantum efficiency of the array of imaging pixels.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for compressive sensing (CS), reconstruction and estimation of ultra-wideband channels of a UWB transmission, comprising the steps of:
building an IR-UWB channel dictionary matrix related to antenna angles of transmission and antenna angles of arrival associated with an IR-UWB signal transmitted over the IR-UWB channel;
using the IR-UWB channel dictionary matrix in conjunction with a measurement basis to perform an action selected from the action groups consisting of reconstructing the IR-UWB signal and estimating the channel of the IR-UWB signal;
when the action group performed is the IR-UWB reconstruction, the step of using the IR-UWB channel dictionary matrix includes the steps of:
(a) combining a measurement matrix \u03a6 with the channel dictionary matrix \u03a8;
(b) measuring the IR-UWB signal using the measurement and channel dictionary matrix combination, thereby producing an output stream of signal measurements;
(c) executing a CS algorithm on the signal measurements using the measurement and channel dictionary matrix combination, the CS algorithm producing a CS algorithm output; and
(d) combining the CS algorithm output with the channel dictionary matrix, thereby reconstructing the IR-UWB signal.
2. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 1, wherein said CS algorithm is a matching pursuit (MP) algorithm.
3. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 2, wherein said MP algorithm has a number of iterations termination criterion, the method further comprising the step of setting the number of iterations termination criterion of said MP algorithm such that a residual ratio=\u2225et\u22252\u2225y\u22252 is restricted to approximately 0.013, whereby the number of iterations is approximately 400.
4. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 2, further comprising the step of using time-domain measured profiles of the UWB transmitted signal obtained from a predetermined antenna as an input to building said channel dictionary matrix \u03a8.
5. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 4, further comprising the steps of:
creating directional profiles utilizing an antenna model having a directional impulse response characterized by the relation:
(t,\u03b8,\u03c6)=\u03a3l=0L\u22121\u03a3k=0K\u22121(t\u2212Tl\u2212\u03c4k,l)\u03b4(\u03b8\u2212\u0398l\u2212\u03c9k,l)\u03b4(\u03c6\u2212\u03a6l\u2212\u03bdk,l),
where \u0398 is the cluster vertical (elevation) angle with uniform distribution on 0, \u03c0), \u03c9 is the ray vertical angle with zero mean of Laplacian distribution, \u03a6 is the cluster horizontal (azimuth) angle with uniform distribution on 0, 2\u03c0), and \u03bd is the ray horizontal angle with zero-mean Laplacian distribution; and
using the antenna model to generate waveforms at receiver elevation angle steps of 0\xb0, 15\xb0, 30\xb0, 45\xb0, and 60\xb0.
6. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 5, further comprising the step of generating atoms of said IR-UWB channel dictionary matrix to create a practical dictionary based on a boresight signal, wherein each atom of the practical dictionary is a shifted version of a practical IR-UWB pulse characterized by the relation:
\u03c8
j
=
p
0
\ue8a0
(
t
–
j
\ue89e
\ue89e
\u0394
)
\ue89e
\ue89e
j
=
0
,
1
,
2
,
\u2026
\ue89e
\ue89e
\u230a
N
–
l
0
\u0394
\u230b
,
where p0 is the practical pulse received at zero degrees and l0 is the length of the practical pulse received at zero degrees.
7. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 5, further comprising the steps of:
generating atoms of said IR-UWB channel dictionary matrix to create a dictionary having variable width atoms, including assigning in said dictionary wider pulse width values corresponding to later arrival pulses associated with increasing relative antenna angles, wherein the pulse duration of the first atom is set to be equal to the live practical boresight duration and zero shift, subsequent atoms having a gradual increase in duration and delay, a last of the atoms having a duration matching the duration of a practical waveform corresponding to a maximum measured reception angle of the antenna, atoms of the variable width dictionary being characterized by the relation:
\u03c8j=p(\u03c3jt\u2212j\u0394) j=0,1,2, . . . ,
where \u03c3j is a parameter controlling the increment of the pulse duration in each of the atoms and the maximum value of j is controlled by the length of the received signal; and
stopping the generation of atoms when shifting exceeds the signal length.
8. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 5, further comprising the step of:
generating atoms of said IR-UWB channel dictionary matrix to create a multi-atom directional dictionary, wherein each atom of the multi-atom directional dictionary is characterized by the relation:
\u03c8
\ue89e
\ue89e
i
j
=
p
i
\ue8a0
(
t
–
j
\ue89e
\ue89e
\u0394
)
\ue89e
\ue89e
i
=
1
,
2
,
\u2026
\ue89e
\ue89e
9
;
j
=
0
,
1
,
2
,
\u2026
\ue89e
\ue89e
\u230a
N
–
l
i
\u0394
\u230b
,
where the first subscript, i, indicates the directional reference number, the second subscript, j, indicates the amount of shift pi(.) as being the ith directional reference, \u0394 is the minimum step shift, and li is the length of the ith directional reference, the multi-atom directional dictionary further being characterized by the relation:
\u03a8
=
\u03c8
1
0
\ue8a0
(
t
)
,
\u2026
\ue89e
,
\u03c8
1
n
–
l
1
\ue8a0
(
t
)
,
\u03c8
2
0
\ue8a0
(
t
)
\ue89e
\ue89e
\u2026
\ue89e
,
\u03c8
9
n
–
l
9
\ue8a0
(
t
)
.
there being 9 directional references.
9. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 8, further comprising the step of generating atoms of said IR-UWB channel dictionary matrix to create a strongest atoms directional dictionary, wherein the strongest atoms directional dictionary is built from the strongest contributed references found in the multi-atom directional dictionary.
10. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 9, wherein said strongest contributed references are atomic references associated with 0\xb0 and 60\xb0, respectively.
11. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 2, further comprising the step of incorporating channel estimation into a compressive sensing full-profile correlator receiver.
12. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 11, further comprising the step of reducing the number of multipath propagation components needing to be estimated by not estimating multipath propagation components in the transmitted IR-UWB signal containing negligible energy.
13. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 12, further comprising the step of performing channel estimation with an FP-correlator to estimate a reference template, the channel estimation being characterized by the relation:
r
p
\ue8a0
(
t
)
=
1
N
w
\ue89e
\u2211
k
=
0
N
w
–
1
\ue89e
r
k
\ue8a0
(
t
)
where rp(t) is the averaged pilot signal, and rk(t) is the received pilot waveforms defined by equation:
r
k
\ue8a0
(
t
)
=
b
p
\ue8a0
(
\u230a
k
N
f
\u230b
)
\ue89e
\u2211
l
=
1
L
\ue89e
\u03b1
l
\ue89e
p
\ue8a0
(
t
–
k
\ue89e
\ue89e
T
f
–
\u03c4
l
)
+
\u03b7
\ue8a0
(
t
)
.
14. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 13, further comprising the step of performing an integration using said reference template to detect symbols transmitted by the IR-UWB signal, the integration being characterized by the relation:
z
\ue8a0
(
k
)
=
\u2211
j
=
0
N
f
–
1
\ue89e
\u222b
j
\ue89e
\ue89e
T
f
+
k
\ue89e
\ue89e
T
f
(
j
+
1
)
\ue89e
T
f
+
k
\ue89e
\ue89e
T
f
\ue89e
r
\ue8a0
(
t
)
\ue89e
\ue89e
r
^
CS
\ue8a0
(
t
–
j
\ue89e
\ue89e
T
f
–
k
\ue89e
\ue89e
T
s
)
\ue89e
\uf74c
t
,
where {circumflex over (r)}CS(t) is the reference frame estimated via MP, and the correlation between the UWB received signal and the template is done inside the integration.
15. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 2, further comprising the step of incorporating channel estimation into a compressive sensing rake receiver.
16. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 15, further comprising the step of using random projections of pilot signals transmitted by the IR-UWB transmitted signal to perform channel estimation, thereby reducing computation and alleviating specious noise in the signal.
17. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 16, further comprising the steps of:
restricting a number of paths to Lc to end up with channel parameters of {{circumflex over (\u03b1)}l,{circumflex over (\u03c4)}l}l=1Lc;
using an MP vector containing estimated delays and path gains resulting from the path restriction to adjust tap delay and gain in fingers of said rake receiver;
associating a correlator with each of the fingers of said rake receiver, each of the fingers being supplied by a template of a transmitted pulse of said IR-UWB signal, said transmitted pulse template being scaled by {circumflex over (\u03b1)}l and delayed by {circumflex over (\u03c4)}l, {circumflex over (\u03b1)}lp(t\u2212{circumflex over (\u03c4)}l), outputs of said correlators representing energy available in the paths selected for estimation to be combined by maximum ration combining;
using said correlator outputs to detect a kth information bit transmitted during a frame of said IR-UWB transmitted signal, the MRC output being characterized by the relation:
z
r
\ue8a0
(
k
,
j
)
=
\u2211
l
L
c
\ue89e
\u03b1
^
l
\ue89e
\u222b
k
\ue89e
\ue89e
T
s
+
j
\ue89e
\ue89e
T
f
+
\u03c4
^
l
k
\ue89e
\ue89e
T
s
+
j
\ue89e
\ue89e
T
f
+
\u03c4
^
l
+
T
p
\ue89e
r
\ue8a0
(
t
)
\ue89e
p
\ue8a0
(
t
–
k
\ue89e
\ue89e
T
s
+
j
\ue89e
\ue89e
T
f
+
\u03c4
^
l
)
\ue89e
\uf74c
t
.
18. The method for compressive sensing, reconstruction and estimation of ultra-wideband channels according to claim 17, further comprising the steps of:
using a pulse index received from channel estimation to select an appropriate pulse shape when a multi-atom dictionary is used;
estimating bits of the kth information symbol by performing a decision computation characterized by the relation:
b
^
\ue8a0
(
k
)
=
{
1
if
\ue89e
\ue89e
\u2211
j
=
0
N
f
–
1
\ue89e
z
r
\ue8a0
(
k
,
j
)
>
0
\ue89e
–
1
if
\ue89e
\ue89e
\u2211
j
=
0
N
f
–
1
\ue89e
z
r
\ue8a0
(
k
,
j
)
\u2264
0.