Optical interconnection networks

Multichannel interconnection networks with optical deformable mirror devices as the reconfigurable switching element.

CROSS-REFERENCE TO RELATED APPLICATIONS 
U.S. application Nos. 777,660, filed Sept. 18, 1985 (abandoned), 901,868, 
filed Aug. 29, 1986 (still pending), and 018,795, filed Feb. 20, 1987 and 
issued Mar. 1, 1988 as U.S. Pat. No. 4,728,185, disclose related subject 
matter. These cross-referenced applications are assigned to the assignee 
of the present application. 
BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The present invention relates to interconnection networks, and, more 
particularly, to multichannel switches using spatial light modulators for 
reconfigurable switching of multiple inputs to multiple outputs. 
2. Description of the Related Art 
Reconfigurable interconnection of several high data rate transmitters to 
several receivers is a cumbersome and technically difficult task. 
Electromagnetic interference has been difficult to control in most 
electronic configurations, and most electronic implementations require a 
dense interconnection scheme that is either difficult or time consuming to 
fabricate. Reconfigurable interconnection networks underlie a variety of 
devices such as high computation rate parallel computing achitectures 
where numerous processors route information to each other or share common 
resources, communications switching as in telephone switching centers, and 
aircraft fiber optic busses that require reconfigurability to allow 
redundancy for fault tolerance and the ability to share several sensors 
with several processors. 
Major trends shaping real time computation include parallel processing and 
symbolic processing. Many real time applications require rapid logical 
decisions using stored knowledge and the processing of large quantities of 
data at high speed. Moreover, close coupling between the symbolic and 
numeric computations is often desirable in fields such as speech and image 
understanding and recognition, robotics, weapon systems, and industrial 
plant control. Indeed, the widespread use of smaller computers in offices 
and homes and the emerging disciplines of artificial intelligence and 
robotics have drawn attention to the fact that an increasing amount of 
computing effort is spent in non-numeric or symbolic computing; many 
software tools used with computers, such as editors, compilers, and 
debuggers, make extensive use of symbolic processing. Symbolic computing 
leads to new methods of solving problems over and above numerical and 
statistical approaches because qualitative information or a priori 
knowledge may be made available in the form of data bases and procedures. 
Attempts to solve real world problems requiring human-like intelligence, 
for example in robotics, speech, and vision, demand enormous amounts of 
symbolic and numeric computing power because of the vast amount of a 
priori information required for what are considered to be simple 
operations and the high data rates from sensors. Indeed, the signal 
processing of sensor data arises in fields such as acoustics, sonar, 
seismology, speech communication, biomedical engineering, etc. and the 
typical purposes of such processing include estimation of characteristic 
parameters, removal of noise, and transformation into a form which is more 
desirable. In the past, most signal processors have been tailored for 
speed and efficiency for a few specific algorithms. Future signal 
processors will need increased speed and algorithm flexibility, so that 
algorithms such as high resolution eigensystem beamforming and optimal 
Wiener filtering may be computed with the same processor and so that new 
algorithms may be efficiently implemented as they are developed. The 
ability to handle a wide range of algorithms in military systems permits 
different algorithms to be used during a mission and field equipment to be 
upgraded with new algorithms. Conventional vector approaches cannot 
satisfy the increasing demand for computer performance and it is necessary 
that future designs be capable of efficiently utilizing extensive 
parallelism, see L. S. Haynes, R. L. Lau, D. P. Siewiorek, and D. W. 
Mizell, Computer 15(1), 9(1982) and J. Allen IEEE Proc., 73(5), 852 
(1985). These references, along with all others herein, are hereby 
incorporated by reference. 
Very large scale integration in semiconductor devices is also leading 
towards the greater use of parallelism. Parallelism requires some sort of 
interconnection between the processing elements and this introduces a 
trade off between speed and the ability to handle a wide range of 
algorithms. For example, a complex interconnection network provides some 
flexibility at the expense of speed, and high speed may be achieved by 
means of fixed interconnections for a specific algorithm. The problem is 
to achieve very high speed by efficiently using a large number of 
processing elements and at the same time retain extremely high algorithm 
flexibility. Efficiency for parallel processing is `the the gain in speed 
versus that using a single processor of the same type` divided by `the 
number of processors`. Also, the complexity of the processing elements 
relates to the degree of parallelism obtainable; sophisticated 
computations tend to have parts that are not parallelizable at a coarse 
level. The overall speed is dominated by the parts which are 
non-parallelizable at a coarse level. And a large number of fast 
elementary processors places a considerable communication burden on the 
interconnection between processors. There is a need for parallel processor 
interconnections that possess simple reconfigurability. 
Currently, most experimental systems have demonstrated the difficulty of 
achieving parallelism for a range of algorithms with even modest numbers 
of processors. The number of parallel processors (hence speed) which may 
be used efficiently is limited in today's prototype and proposed systems 
by the communication delay and interconnection complexity. The constraints 
imposed by the interconnections on algorithm design are a serious problem 
because they reduce opportunities to achieve performance by new algorithm 
design and raise cost by limiting the range of applications and the 
lifetime of the equipment. 
Fixed interconnections limit the range of algorithms which may be 
efficiently implemented. Systolic configurations, such as those in 
development at Carnegie-Mellon University (Kung H. T., Why Systolic 
Architectures?, IEEE Computer, Jan., 1982 p37-46), use algorithm structure 
to reduce memory and instruction fetches. This reduces communication time 
and permits large numbers of processors to be efficiently used in 
parallel. However, the algorithm constraints are significant because of 
the fixed interconnections. 
Algorithm flexibility may be achieved by complex reconfigurable 
interconnection networks, and a prototype system having 8 processors and 
using a Banyan switch is in operation at the University of Texas at Austin 
(Browne J. C., Parallel Architectures for Computer Systems, Physics Today, 
Vol. 37, No 5, May 1984). A Banyan is a multichannel switch composed of 
levels of 2.times.2 switches. However, this type of reconfigurability 
introduces large delays and high control overhead in most proposed systems 
and this restricts the number of processors and the speed of the system. 
The distribution of effort amongst a number of processors does not remove 
the need for some minimum level of central control, although, for fault 
tolerance purposes this may not always be the same physical part of the 
system. The idea of a single program which alone determines the complete 
operation of the computer is replaced by numerous such programs running 
concurrently in different processors. The communication channel to the 
central control must be sufficient to prevent it from becoming a 
bottleneck. And common memory is frequently used in the process of 
communicating information from one processor to another. A potential 
difficulty, memory contention, arises when two or more processors request 
the same piece of information form a common memory at the same time. Some 
arbitration is now required and one processor will have to remain idle or 
make the memory request again later. This increases complexity, cost and 
inefficiency. A simple example arises in matrix-matrix multiplication 
where a single row of a first matrix is required in all processors for 
simultaneous multiplication with each column of a second matrix. Memory 
contention for such well-defined operations should be taken care of in the 
computer design. 
Great skill is required to partition problems so that various processors 
complete their tasks at the appropriate time to provide information for 
the next stage. Synchronization forces everything to wait for the slowest 
link with resulting inefficiency. A parallel algorithm may involve more 
steps than a commonly used serial algorithm even though it is more 
efficient on a specific parallel machine. The overhead reduces the 
efficiency of the algorithm where efficiency is measured as the speed on 
the multi-processor divided by the speed with the fastest algorithm on a 
single processor. The stability and accuracy of the parallel algorithm 
relative to the serial algorithm must also be considered in comparison. 
The communications industry makes widespread use of optical fibers and is 
developing optical switching devices to avoid conversion to electronics 
and back for switching purposes. Optics has been suggested for 
communication with VLSI to overcome the bandwidth pin limitations and edge 
connection constraints; see Goodman J. W., Leonberger F. J., Kung S. Y. 
and Athale R. A., Optical Interconnections for VLSI Systems, Proc. IEEE, 
Vol. 72, No. 7, July 1984, p850-866. 
Digital optical computers are expected to eventually become dominant and a 
design has been proposed for solving a major class of problems, finite 
elements (see McAulay, Deformable Mirror Nearest Neighbor Optical 
Computer, to appear in Optical Engineering (1985) and abandoned U.S. appl. 
Ser. No. 777,660). This design uses deformable mirrors or other spatial 
light modulators (see Pape D. R. and Hornbeck L. J., Characteristics of 
the Deformable Mirror Device for Optical Information Processing, Opt. Eng. 
Vol. 22, No. 6, Dec. 1983, pp. 675-681). Machines using acousto-optics for 
matrix algebra operations are in research. These computers, although 
significant for numerical computation, have limited algorithm felxibility 
because of the interconnection systems used. They are also not aimed at 
signal processing applications. 
Small commercial crossbar switches made entirely of semiconductor devices 
have recently become available; see the description of the AS8840 chip 
from Texas Instruments on pages 72-73 of Electronics for Feb. 5, 1987. The 
AS8840 is a 16-port crossbar integrated circuit which is dynamically 
reconfigurable; each of the ports handles a nibble (four bits) 
bidirectionally. 
D. Grant et al, An Optical Phased Array Beam Steering Technique, 1971 
Proceedings of the Electro Optic System Design Conference pages 259-264, 
describes reflection of collimated light from a membrane spatial light 
modulator is passed through a sampling mask and the phase variations 
caused by the pixels of the spatial light modulator combine to form a 
single spot on a receiver array. Varying the pixel deformations controls 
the phase variations and thereby steers the spot across the receiver 
array. 
However, the known optical interconnection networks for high speed data 
transmission are limited in size by inefficiencies in handling the optic 
power. 
SUMMARY OF THE INVENTION 
The present invention provides optical interconnection networks that use 
deformable mirror devices with on-axis optics including diameter-limited 
imaging lenses and optical fiber bundles for high efficiency with high 
speed detectors. Preferred embodiments use a tightly packed staggered 
bundle of single mode optical fibers for input to insure image 
magnification to the deformable mirror device and consequent decrease of 
ligh bundle numerical aperture. The small light bundle numerical aperture 
allows large deformable mirror devices to be imaged on small high speed 
detectors.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
A preliminary description of reconfigurable optical interconnection 
networks will be followed by descriptions of the preferred embodiments. 
Generally, an interconnection network permits N inputs to be connected to 
K outputs with each output equal to one of the inputs. 
FIG. 1A schematically illustrates a 4 by 4 crossbar switch 102. Each 
intersection has a directional switch permitting a horizontal input line 
to be coupled with a vertical output one. Black circles indicate closed 
switches. One ouput receives information from one input, but one input may 
broadcast to several outputs. FIG. 1B shows a diagrammatic crossbar switch 
102 implemented with spatial light modulator 130 and dots indicate 
transparent regions consistent with the settings of FIG. 1A. An optical 
lens system (not illustrated) is used to spread the light from the input 
sources (LEDs 1-4) horizontally without spreading the light vertically. 
Light passing through spatial light modulator 130 is collapsed onto 
receiving diodes (Detectors 1-4) by means of a lens system (not 
illustrated) which focusses vertically without spreading horizontally. 
Information is transmitted through switch 102 by encoding the information 
as modulation of the transmitter light beams and received by demodulating 
the received signals. 
FIG. 2 shows an optical crossbar switch as a communication link consisting 
of transmitter, receiver, and the transmission medium. The transmission 
medium includes short lengths of optical fiber and openair optics. 
Compared to long distance links, where the large fiber attenuation 
increases the signal to noise ratio prior to the detector, losses in a 
crossbar are due primarily to reflection, and noise contribution is due 
primarily to the receiver without consideration of transmitter laser 
noise. 
FIG. 3 illustrates switch 102 as implemented by a deformable mirror device 
(DMD) as the spatial light modulator 130 together with connections to the 
switch for a plurality of processors. DMDs act as variable intensity 
reflectors rather than transparent modulators, consequently, the right 
side of the modulator is folded back. A beam splitter 132 is used to 
separate the returning light from the incident light. (Alternatively, 
tilting DMD 130 relative to the optic axis may avoid a beam splitter.) 
Schlieren optics 134 are used to block reflections from the regions 
between pixels and from undeflected pixels of DMD 130. Laser diodes 136, 
with modulation capability to 3 GHz will act as sources and p-i-n diodes 
138 as detectors. The optics not illustrated in FIG. 1B are schematically 
shown in FIG. 3: cylindrical optics 140 spreads the light from the input 
sources 136 horizontally and cylindrical optics 142 collapses the light 
vertically onto the receiving diodes 138. Of course, the lenses 
illustrated are just functional representations of quite complex optics in 
practice. The light sources 136 and receivers 138 could be integrated 
directly on the electronic chips. 
Deformable mirror devices of the membrane and cantilever type have been 
developed. The results for imaging and performing spectral analysis with a 
Texas Instruments membrane DMD have been published; see D. R. Pape, L. J. 
Hornbeck, Opt. Eng., 22(6), 675 (1983), and description of the Texas 
Insturments cantilever beam DMD appears in L. J. Hornbeck, U.S. Pat. No. 
4,566,935. A cantilever beam DMD typically is an X-Y array of deformable 
mirror elements that can be addressed by an underlying array of MOS 
transistors; see FIG. 4A for a perspective view of a mirror elements and 
FIG. 4B for a schematic view of the array. A reflecting conductive metal 
layer 26 covers the surface of the array and has cutouts forming mirrors 
28. The line-addressed organization of the DMD is shown in FIG. 4B; data 
are fed to a serial-to-parallel converter 171 that is connected to the 
drain lines 172 of the MOS transistors. Drain lines 172 are charged (the 
k.sup.th line 172 is charged to a potential .phi..sub.k,m), and decoder 
174, connected to gates 176, selects the m.sup.th gate to turn on. 
Floating sources 178 of the MOS transistors in the m.sup.th gate line 177 
are then charged to the potential of the corresponding drain 172 (the 
m.sup.th charged to .phi..sub.k,m). The gate is then turned off, and 
mirror 28 is held at a fixed potential of V.sub.M ; thus an electrostatic 
force proportional to V.sub.M -.sub.k,m acts on the (k,m).sup.th mirror 
element and causes it to deflect down towards the floating source 178. The 
mechanical response time of a mirror element and hence line settling time 
is a few .mu.sec. Once the floating sources 178 in the m.sup.th gate line 
177 have been set, then the next line of data is fed into drain lines 172, 
and the next gate line 177, selected by decoder 174. The deflection of the 
membrane or beam is a nonlinear function of the applied voltage and 
approximates the form illustrated in FIG. 4C; note that above a critical 
"collapse voltage" the membrane or beam is unstable against collapse to 
the charged capacitor plate. Of course, there is a range of voltages in 
which the cantilever beam deflection may be reliably controlled. The size 
of the mirror elements for both the membrane and cantilever beam devices 
is in the order of 25 microns square. 
FIG. 5 is a detailed layout for the optical crossbar switch shown in FIG. 3 
with some rays traced to illustrate the optics. FIG. 5 shows the crossbar 
switch from three views; note that the system lacks rotational symmetry 
and that some lenses (cylindrical lenses) only focus in one view. Also 
note that for clarity the DMD is shown as being a transmissive rather than 
a reflective five-by-five device. The five inputs are five intensity 
modulated laser diodes, and the lasers' light 136 which typically may be 
100 .mu.m diameter fibers spaced about 100 .mu.m apart. The fibers can be 
closely packed over a short propagation length, while the laser diodes can 
be separated for electromagnetic isolation. In the top view of FIG. 5 the 
light from the fibers is diverged to the width of a row of pixels in DMD 
130. Lens 144 collimates the light to the plane of DMD 130. FIG. 5 
illustrates fibers 151, 153, and 155 emitting light which illuminates 
pixel rows 161, 163, and 165 of DMD 130; DMD 130 is configured with only 
one pixel in row 161 and one pixel in row 165 deflected. 
The collimated light reflects off of DMD 130 with the light reflecting from 
undeflected pixels and the area between the pixels having low spatial 
periodicity and thus being highly attenuated by the Schlieren stop 146 
(this is illustrated by the dotted lines in the top view of FIG. 5). 
Contrarily, the light reflecting from deflected pixels will be phase 
modulated at a high spatial frequency and pass around Schlieren stop 146 
with only mild attentuation as illustrated by the dotted line circles in 
the cross view of FIG. 5. The light passing Schlieren stop 146 is reimaged 
onto horizontal array 138 of receiving optical fibers with the light from 
a column of pixels of DMD 130 imaged onto a single receiving fiber. In 
FIG. 5 the second pixel of row 165 and the fourth pixel of row 161 are 
deflected and illuminated, so the second and fourth receiving fibers are 
illuminated. Ideally, only the light from the deflected pixel in one DMD 
row is imaged onto the appropriate output receiving fiber; however, a 
portion of light from the other undeflected rows (transmitted from the 
other input array fibers) will diffract around the vertical Schlieren stop 
and be focussed onto the same receiving fiber as the deflected light 
signal of interest. Thus care must be taken in design of both horizontal 
and vertical stop dimensions to maximize deflected to undeflected energy 
reaching receiving array 138. 
First preferred embodiment optical interconnection network, schematically 
illustrated in FIG. 6 with a four by four DMD, is similar to the network 
illustrated in FIGS. 3 and 5 but uses undeflected pixels in DMD 230 to 
correspond to interconnections rather than deflected pixels as in DMD 130. 
As before, the lenses are just functional representations. FIG. 6 
illustrates the interconnection of the second transmitter row to the third 
receiver column by deflecting all pixels except the one in the second row 
and third column of DMD 230 (the image inversions caused by the lenses 
have been omitted for clarity). The image reflected off of DMD 230 is 
reimaged by lens 245 onto sampling mask 246 which is used to block out 
light reflected from the areas of DMD 230 between the pixels. Deflected 
pixels reflect incoming light beyond imaging lens 242 as described below. 
This use of lens 242 rather than Schlieren stop 146 has the advantage of 
utilizing the on-axis light for the interconnections, rather than the 
light reflected off deflected pixels, and this implies the use of smaller 
diameter lenses and less time dispersion in the received signal. 
Several sources of loss, such as glass attenuation, metal reflectivity, 
beam splitter loss, inactive area of DMD 230, fanout of transmitter light 
across an N pixel row, nonuniform illumination of rows of DMD 230, 
illumination of finite aperture lenses, and imperfect contrast between on 
and off states, occur in the first preferred embodiment; and estimation of 
the latter three of these losses will help further describe features of 
the first preferred embodiment. In particular, to obtain useful large 
interconnection networks the inputs will need to be of small size and low 
numerical aperture (NA) and tightly packed together to permit 
magnification by imaging lens 240, and the demagnification from DMD 230 to 
detectors 238 will need to avoid loss from lens 242 overfill. 
The row illumination of a DMD 230 row is derived from a circular beam that 
is compressed vertically by anamorphic lens 240; see FIG. 9 which 
illustrates the geometry. The light energy density at the end pixels of a 
row will typically be smaller than at the central pixel, and thus the best 
energy utilization is for maximization of the light available to the least 
illuminated pixel. This can be approximated in closed form by presuming 
uniform light intensity out to a radius of R emitted from a point source 
(optical fiber 136) with small angular divergence. FIG. 9 shows the 
uniformity of row illumination to depend on .alpha.as 
.sqroot.1-.alpha..sup.2 and the efficiency (ratio of light intercepted by 
a row to total light emitted by a source) to depend on .alpha. as 
##EQU1## 
and the product of uniformity times efficiency is illustrated in FIG. 10. 
FIG. 7 is a simplified model of the optics system from DMD 230 to detectors 
238; the sampling mask optics and horizontal imaging lens have only minor 
effects on contrast and vertical lens aperture collection efficiency. DMD 
230 will be presumed to have N rows and columns for clarity. Each column 
of pixels of DMD 230 is imaged onto one detector 238 of width L.sub.D with 
magnification 
##EQU2## 
where S.sub.1 is the distance from DMD 230 to imaging lens 242, S.sub.2 is 
the distance from imaging lens 242 to detector 238, N is the number of 
rows and also the number of columns of pixels in DMD 230, and w is the 
periodicity of the pixels; see FIG. 8 which shows a plan view of DMD 230 
and note that the cantilever beams are hinged to deflect parallel to the 
columns and not diagonally as indicated by FIG. 4A. High speed, high 
sensitivity detectors have typical diameters of 70 .mu.m due to dark 
current and capacitance limitations; thus L.sub.D should be about 70 
.mu.m. In view of the typical value of w of 50 .mu.m (25 .mu.m cantilever 
beam plus 25 .mu.m spacing) and N of 1,000, M is small and significant 
demagnification is required. Note that DMD pixels must be large enough to 
avoid diffraction loss as well as permit magnification at input, so the 
demagnification M cannot be avoided by just using small pixels. For this 
arrangement, light collection efficiency is influenced by the light bundle 
spread, .theta..sub.IN, off individual pixels of DMD 230, the vertical 
diameter, D, of lens 242, and the height, Nw, of DMD 230. FIG. 7 shows 
both that the minimum light power from indeflected pixels is collected 
from the top row pixels, and that for complete removal of light from 
deflected pixels to avoid crosstalk requires the cantilever beam 
deflection angle, .theta..sub.DMD, satisfy 
##EQU3## 
in order to remove all the light from a deflected pixel in the bottom row. 
Applying this analysis to the DMD geometry with the Schlieren filter shows 
that a larger lens or a decentered lens or lenses is required, but this 
limits the size of the DMD for a given performance and large lenses are 
difficult to fit into a small space. Note that the cantilever beam 
deflection angle is fixed; that is, a set voltage is applied to a pixel to 
deflect it, and the possibility of angle dependence upon applied voltage 
is not used. Furthermore, the inequality can be written in terms of a 
fundamental set of design variables .gamma. and C to be defined infra. 
First, for lens 242 the focal length, f, is related to the distances 
S.sub.1 and S.sub.2 by the thin lens formula: 
##EQU4## 
and recalling that M is small shows that S.sub.1 approximately equals f 
times M. Next, the aperture of lens 242 relative to its focal length is 
defined by 
##EQU5## 
The parameter .gamma. defined by lens 242 vertical diameter to spot 
diameter at the lens 242 can be shown to be 
##EQU6## 
where NA.sub.IN is approximately .theta..sub.IN because .theta..sub.IN 
will be shown to be small, that is, NA.sub.IN approximately equals 
sin(.theta..sub.IN). 
The parameter C defined as DMD height to lens diameter, 
##EQU7## 
then the inequality can be written as 
##EQU8## 
For a DMD with pixels above the center line of the DMD deflecting upwards 
and pixels below the center line deflecting downwards the inequality 
becomes 
##EQU9## 
and is more easily satisified. For either form of the inequaltiy, 
NA.sub.IN is limited to be reasonably small by the limited deflection 
range of the pixels, which is approximately 0.12 radians. (Extreme 
deflection is also undesirable because it reduces cross sectional 
intersection with light and thus increases the stray light from 
diffraction.) For .gamma. equal to zero, NA.sub.IN could be no larger than 
0.24, and .gamma. cannot be negative. With NA.sub.IN limited to about 
0.24, and noting that the light bundle spread changes by the reciprocal of 
the magnification from input array 136 to DMD 230, the input optical 
fibers must be small diameter, low divergence sources. Using multimode 
fiber as a source would be unacceptable with the typical DMD cantilever 
beam dimensions of about 25 .mu.m; for example, a standard 0.2 NA, 50 
.mu.m core fiber spaced at 100 .mu.m for imaging onto consecutive DMD 
rows. A magnification of 0.5 is thus required and leads to an NA.sub.IN of 
0.4, which is too large. Single mode optical fiber with NA equal to 0.1 
and less than 10 .mu.m core diameter for typical laser diode sources 
(wavelengths in the range of 0.84-1.55 .mu.m) and a 20 .mu.m core-to-core 
spacing and a 2.5 demagnification will map the light bundle NA to 0.04 at 
DMD 230. For a wavelength of 1 .mu.m, the incident bundle gives a 99% 
energy encirclement diameter of 24 .mu.m. Thus there is minimal 
diffraction in reflecting off of the cantilever beam and NA.sub.IN would 
equal the NA of the incident light bundle. This diffraction limited 
condition also points to optimum design of the DMD geometry and the lens 
aberration specification for lenses between the input fibers and the DMD. 
That is, to minimize diffracted and overlap loss from the DMD, we want to 
just fill a DMD row height which implies the required lens imaging 
properties. 
The geometric terms .gamma. and C also are design parameters that describe 
the ability of lens 242 to collect light. Previously NA.sub.IN was limited 
to avoid crosstalk; FIG. 7 also shows that small NA.sub.IN helps 
collection efficiency from undeflected pixels provided that the vertical 
diameter D of lens 242 is greater than the height Nw of DMD 230. The 
collection efficiency of lens 242 can be defined by the intensity ratio of 
light from the top undeflected pixel intercepted by lens 242 to the total 
light reflected from the pixel. The 1:1 horizontal imaging lens 242, and 
all light intercepted by lens 242 is presumed collected by detectors 238. 
Computing the lens 242 collection efficiency is routine in view of the 
derivation in connection with FIG. 9 and yields the results in FIGS. 11 
and 12. For large .gamma. the central spot underfills lens 242; and a DMD 
230 width increases, the extreme spot moves outside the lens aperture. 
Because the spot diameter is small compared to lens 242 aperture, the 
curve varies rapidly from unity to zero efficiency. For small .gamma. the 
central spot overfills lens 242 and accounts for most of the loss. 
Efficiency becomes nearly independent of C over this range because the 
extreme spot has an almost rectangular overlap of nearly the same 
dimensions as the central spot. In the small .gamma. region, efficiency 
can also be seen to be independent of C, as well as asymptotically linear 
in FIG. 12. In this region the efficiency is approximately equal to 
##EQU10## 
which is inversely proportional to N by the previous equations. For energy 
efficient design, this approximation defines the region in which the lens 
is overfilled. The largest possible crossbars will probably overfill the 
lens because practical lenses are limited in numerical aperture and thus 
are described by this approxmiation. This result will significantly 
simplify predictions for maximium size DMD for an N by N crossbar. 
Rewriting and combining this with the efficiency result expressed in FIG. 
10 and other standard system estimates, an energy budget may be generated 
as in FIG. 13. Before interpreting FIG. 13, its derivation will be given. 
Note that receiver array 238 could be either an open air detector array 
(probably monolithic) or an optical fiber bundle, the fibers of which are 
connected to discrete detectors located a short distance away from array 
238. Detector arrays offer the advantages of large acceptance angle and 
compact integration compared to discrete detectors, but sensitivity and 
immunity to electical crosstalk are problems. Consequently, both optical 
fiber bundle and detector array versions of receiver array 238 are 
possible. And with the great demagnification by lens 242 the maximum 
effective NA.sub.lens is limited to the acceptance angle, NA.sub.fib, of 
the optical fiber; thus multimode fiber would be used. 
Table I contains estimates for the significant terms for computing the loss 
in traversing a node of the crossbar switch and thus an energy budget. 
Transverse single mode lasers can ideally be lens coupled to single mode 
fibers with no loss if perfect alignment is maintined. Also in practical 
designs, losses result from the overfilling of pixels and consequent 
diffractive spreading unless DMD pixels are of adequate size. 
______________________________________ 
Variable Range 
______________________________________ 
DMD size (N) 1-10,000 
Transmitter power (P.sub.in) 
30 m W 
Detector sensitivity for 
2.2 .times. 10.sup.-6 to 
10.sup.-9 bit error rate 
1.8 .times. 10.sup.-4 m W 
AR coated glass efficiency 
0.85 
Beam splitter efficiency 
0.25 
Fiber and splice efficiency 
0.65 
Pixel reflectivity 0.90 
Undeformable surface 
0.5 
Spread across DMD row 
##STR1## 
Spread wider than DMD row 
0.55 
Demagnification to detector 
##STR2## 
Fiber to detector coupling 
0.49 to 1 
Transmitter to fiber 
1 
Vertical overfill pixel 
1 
______________________________________ 
The individual terms may be combined to yield the overall power available 
at the detector: 
##EQU11## 
P.sub.avail must be no smaller than P.sub.min, the minimum detectible 
power for a specified bit error rate. Note that the receiver sensitivity 
range is representative of 320 Mbs PINFET (dashed line in FIG. 13), 3.2 
Gbs avalanche photodiode (broken line in FIG. 13), and 3.2 Gbs quantum 
limit (solid line in FIG. 13). The curves in FIG. 13 result from inserting 
the parameters for the three receiver types into the power available 
expression. The two vertical curves in FIG. 13 are included to illustrate 
the effect of crosstalk on DMD size. These curves represent sensitivity 
derating due to equal contribution of light at a level of C.sub.t below 
the "on" pixel intensity, from N-1 "off" pixels. Thus the DMD size N is 
limited to the size indicated by the intersection of the energy budget 
curve corresponding to the detector type and the derating curve; and there 
is a natural boundary at N.sub.max =1/C.sub.t if the pixel-induced 
crosstalk is substantially larger than P.sub.min. 
In summary, the results for asymptotic efficiency embodies design 
constraints and rationale as asymptotic efficiency equals 
##EQU12## 
First make L.sub.D as large as possible, but it is limited by speed 
considerations; secondly, Nw is desirably as large as possible for more 
interconnectivity, but this reduces efficiency and w is limited by 
diffraction; make NA.sub.lens as large as possible, but practical 
limitations exist such as the requirement of high refractivity materials 
and aberrration, and make NA.sub.IN as small as possible but this is 
limited by source input and DMD size constraints. 
Second preferred embodiment optical interconnection network is similar to 
the first preferred embodiment but uses input array 236 illustrated in 
cross view in FIG. 14 in place of input array 136. Optical fiber bundle 
236 uses optical fibers with 10 .mu.m diameter cores and cladded to a 
total diameter 120 .mu.m; thus the vertical spacing between core centers 
is 20 .mu.m. The 120 .mu.m total diameter and the 20 .mu.m 
center-to-center prescribe the six-fiber width of bundle 236; the 
horizontal spread of 592 .mu.m will cause only small variation in thge 
uniformity of illumination between rows of DMD 130 if the light is allowed 
to spread somewhat wider than the bundle width. Of course, the network 
illustrated in FIG. 6 only has four input fibers shown for clarity; the 
general situation as illustrated in FIG. 3 may have 1,000 input fibers. 
For 1,000 input fibers, bundle 236 would have 167 staggered rows of six 
fibers per row. And the broad valley of the curve in FIG. 10 indicates 
that the illumination variation due to the staggering of bundle 236 can be 
made small for a small bundle width. For the approximately 600 .mu.m 
bundle width of bundle 236 and the 50 .mu.m spaced cantilever beams of 25 
.mu.m size, FIG. 10 shows that a 2.5 dB overall loss would be possible for 
as few as 40 pixel rows. 
Variations of the bundle of FIG. 14 include the following: if the fiber 
core has diameter d and the cladded total diameter is D, then a core 
center vertical spacing of D/n is achieved by staggered rows of n fibers 
and this core center spacing may be less than the core diameter d. 
Third preferred embodiment interconnection network is similar to the first 
preferred embodiment but uses DMD 330 as a blazed grating; that is, all 
pixels in a row are deflected by the same angle and this angle is 
adjustable. See FIG. 15 for a plan view of DMD 330 illustrating the 
hinging of the cantilever beams so that all the pixels in a row may be 
deflected to form a one-pixel wide blazed grating as shown in the FIG. 16 
which is a cross sectional elevation view along line 16--16 of FIG. 15. 
Thus each row of DMD 330 is used as a scanner; the diffraction pattern of 
collimated light off of a row gives a peak that has a resolution equal to 
the number of pixels in the row. The angle of deflection then determines 
the position of the spot in the diffraction plane and thus which receiver 
is illuminated. Lens 243 is a Fourier transform lens and not an imaging 
lens. This operation is analogous to an acousto-optic Bragg cell 
scanner/deflector. It would be extremely costly and bulky to built a 
scanner with as many channels as a typical DMD (128 to 1,000). Also, the 
DMD is simplified in that only one analog driver is required for each row 
of pixels because all pixels in a row have the same applied voltage and 
deflection. In contrast to the first preferred embodiment, the third 
preferred embodiment can scan the light from a single row to only one 
output receiver in array 238 at a time. Each of the N rows can be scanned 
to a particular output position, and the network would then be able to 
form any permutation of input channel with output channel. The coherent 
addition of light from the individual periodically spaced deflected pixels 
forms a much narrower peak intensity pattern. If the diffraction intensity 
pattern of a single deflected pixel is sinc.sup.2 x, then the diffraction 
pattern of N deflected pixels will be approximately N.sup.2 sinc.sup.2 Nx. 
The central lobe of the reflected light beam is deflected specularly based 
on the blaze angle, the angle of incidence, and the wavelength. The angle 
of deflection in a pixel varies continuously with the applied voltage 
(FIG. 4C), and thus the blaze angle can be controlled to move the spot to 
the desired output receiver. 
MODIFICATIONS AND ADVANTAGES 
Various modifications of the preferred embodiment devices and methods may 
be made. For example, the DMD could have various types of deflectable 
beams such as torsion beams (hinged at two points and twisting along the 
hinges), the optics systems shown as simple ideal lenses could be multiple 
element systems with various folds in the optic axis, the laser diodes 
could be other types of light emitters, the DMD areas between pixels could 
be darkened in order to eliminate the sampling mask, and so forth. 
The invention provides the advantages of large size networks by the 
efficient direction and low losses of the interconnecting light beams.