Optical learning neurochip with internal analog memory.

An optical neurochip with learning capability and a memory function is reported for the first time, to our knowledge. The neurochip is a three-dimensional optoelectronic integrated circuit consisting of a light-emitting diode array and a variable-sensitivity photodetector array. The principle of operation and the fundamental characteristics of the neurochip are described in detail. The synaptic weights, which, are acquired through learning, are stored in the variable-sensitivity photodetector as the detection sensitivity with analog values. Both the positive and the negative synaptic weights are memorized with one variable-sensitivity photodetector element by changing the polarity of the electric signal applied to the photodetector. A storage time of ~20 min. was obtained. With a fabricated optical neurochip that had 32 neurons and 32 x 32 synapses, experiments of on-chip learning for pattern classification were performed successfully.

N x N to N pattern associative memory by using optoelectronic neurochips.

A scheme of N x N to N pattern associations based on a fully connected neural network is proposed. The connection weight matrix of the neural network with N neurons is defined by the input pattern of size N x N through a linear transformation. The point attractor of the system state for this input pattern is then used as the associative output to identify the input pattern. This scheme can be implemented by the optoelectronic neurochips in a simple fashion. The learning algorithm, the simulation results, and the implementation are presented.

Variable-sensitivity photodetector that uses a metal-semiconductor-metal structure for optical neural networks.

A novel type of photodetector called a variable-sensitivity photodetector has been developed for optical implementation of neural networks. It utilizes a metal-semiconductor-metal structure whose quantum efficiency can be modulated by an applied bias voltage. A linear dependence of the sensitivity on the bias voltage was obtained with the bipolar current flow. This device operated as a multiplier of the incident light intensity and the bias voltage. It is shown that this device is suitable for achieving dynamic synaptic interconnections. A 4 x 4 array device was fabricated and demonstrated.

Dynamic optical neurochip using variable-sensitivity photodiodes.

A novel type of a dynamic optical neurochip that uses sensitivity-variable photodiodes (VSPD's) as variable interconnection weight elements is proposed and analyzed. The chip consists of the line-shaped light-emitting diode array and the VSPD matrix array. A monolithic integration of these arrays is presented. This dynamic chip has advantages such as less optical cross talk, higher processing speed, and realization of analog synaptic weights. Also, experimental results of the VSPD array using metal-semiconductor-metal structure are shown. The results of the computer simulation using this chip as a learning element show that the unwanted effect of the optical cross talk on the recognition rate can be alleviated by the learning capability of the chip. It is also found that the theoretical maximum density is ~2000 neurons/cm(2) for the backpropagation model.

Optoelectronic associative memory using an advanced optical neurochip.

A drastic improvement in the performance of the associative memory was achieved using an optical neurochip. The experimental recognition rate agreed well with computer simulation for the associative memory.

Optical implementation of large-scale neural networks using a time-division-multiplexing technique.

A new architecture for optical implementation of large-scale neural networks is proposed. This architecture is based on a time-division-multiplexing technique, in which both the neuron state vector and the interconnection matrix are divided in the time domain. Computer simulation and experimental results for associative memories show the effectiveness in implementing large-scale networks.

Optical flip-flop based on parallel-connected AlGaAs/GaAs pnpn structures.

An optical flip-flop using parallel-connected pnpn (thyristor) structures is demonstrated for the first time to our knowledge. We utilize the differential optical switching technique for inverting the state of the flip-flop and the slow dissipation of carriers stored in the gate layers of pnpn structures for retaining the state. The flip-flop operation with 7.2 pJ of optical input energy was attained by using an AlGaAs/GaAs device. The expansibility to monolithic two-dimensional arrays, the high on-off contrast, and the high optical gain of the flip-flop are useful in the design and fabrication of optical parallel processing systems.

Character recognition using a dynamic optoelectronic neural network with unipolar binary weights.

A novel type of quantized learning rule with unipolar binary weights that is useful for the optical implementation of neural networks is reported. An input-dependent thresholding operation is also proposed to remove the unwanted effects that are due to the insufficient contrast ratio of spatial light modulators as synaptic connection devices. Moreover, we experimentally demonstrate the recognition of 26 characters of the alphabet by using the single set of an optoelectronic three-layered network.

Optical neurochip based on a three-layered feed-forward model.

We report on a GaAs/AlGaAs optical neurochip based on a three-layered feed-forward model. The optical neurochip consists of a light-emitting diode array with 66 elements, a fixed interconnection matrix, and a photodiode array with 110 elements. The interconnection matrix is determined by the backpropagation learning rule with three quantized levels. There are 35, 29, and 26 neurons, respectively, in the input, hidden, and output layers. The excitatory and inhibitory synapses are integrated on one chip. By using the chip and external electronics, we have succeeded in the recognition of 10 characters with 5 x 7 bits.

GaAs/AlGaAs optical synaptic interconnection device for neural networks.

A GaAs/AlGaAs optical synaptic interconnection device for neural networks is reported for the first time to our knowledge. This device consists of a light-emitting-diode array, an interconnection matrix, and a photodiode array, which are integrated into a hybrid-layered structure on a GaAs substrate. The device structure and characteristics are reported in detail. The fabricated device can simulate a 32-neuron system. Experimental results of the Hopfield associative memory with three stored vectors are also described.

Optical implementation of an associative neural network model with a stochastic process.

An optical associative neural network with a stochastic thresholding procedure has been demonstrated. The use of stochastic processing drastically improved the convergence rate into the correct global minima (recognition rate). The properties of undesirable spurious minima were also investigated. It was found that the spurious minima were represented as the mixed states of the stored vectors. A useful method to estimate the required noise level to vanish the spurious minima is described.

Fiber-optic passive ring-resonator gyroscope using an external-cavity laser diode.

We report a fiber-optic passive ring-resonator gyroscope that uses an external-cavity laser diode. The external-cavity laser diode is useful as the light source for the gyroscope if an appropriate cavity length is chosen. A rotation detection sensitivity of 10(-2) rad/sec was obtained with an integration time of 10 sec.

Effect of reflections on the drift characteristics of a fiber-optic passive ring-resonator gyro.

We show the effect of reflections at the fiber ends on the drift characteristics of a fiber-optic passive ring-resonator gyro. A rotation-detection sensitivity as low as 3 x 10(-5) rad/sec (tau = 30 sec) was obtained by reducing the effect of reflected lights with an optical phase modulator.

Optical phase-conjugate correction for propagation distortion in nonreciprocal media.

We demonstrate experimentally that the effects of nonreciprocal elements or media that would otherwise spoil phase conjugation can be neutralized by using a tandem combination of a modal- and polarization-scrambling fiber and a phase-conjugate mirror. A theoretical model to explain the experimental results is presented.

Demonstration of amplitude-distortion correction by modal dispersal and phase conjugation.

We demonstrate experimentally and explain theoretically polarization-preserving imaging through a lossy amplitude-distorting medium. This is accomplished by propagating the beam, before its arrival at the lossy distorting medium, through a (multi) mode- and polarization-scrambling fiber and reflecting the signal, after it has passed the lossy distorting medium, from a photorefractive phase-conjugate mirror.

Theoretical model for modal dispersal of polarization information and its recovery by phase conjugation.

A theoretical model is proposed for explaining the recently observed modal dispersal of polarization information and its recovery in an experiment in which the coupling dispersing fiber. to the phase conjugator is by means of a (multi)mode dispersing fiber.

Laser Doppler velocimeter with a novel optical fiber probe.

A practical laser Doppler velocimeter with optical fibers in the whole system was developed. The novel optical probe designed for this LDV is constructed of a graded-index rod lens attached to the end of an optical fiber. Since the laser beam from the probe is well collimated, the velocity accuracy and sensitivity are significantly improved. Mechanical vibration measurements were also carried out with this LDV; vibration amplitude down to 1.0 microm p-p can be measured at a frequency of 120 Hz with high accuracy.