Johannes Joseph Hubertina Barbara Schleipen

Small form factor optical drive: miniaturized plastic high-NA objective and optical drive

Recent developments in portable consumer devices call for storage systems solutions using compact drive units and cheap storage media. A major advantage of conventional optical storage is the intrinsic low media cost and the ease of manufacturing of replicated ROM media. Third generation optical storage, using a blue laser and a high numerical aperture objective lens, is a perfect technology candidate for a small form factor optical (SFFO) drive. Using third generation optical storage technology 27 GBytes becomes available on a 12 cm optical disc. Using this data density for an SFFO drive, a storage capacity of over 1 Gbytes become feasible on a coin-sized disc. In this paper we report the realization of a SFFO drive, featuring 1 Gbytes on a 30 mm rewritable optical disc, with dimensions comparable to Compact Flash PCMCIA-like drives. Our main focus in this paper is on the miniaturization of the basic components of the SFFO drive, such as disc, objective and 2D-actuator. Related subjects in Philips R&D are miniature optics and low-dissipation/high-integration electronics.

Neural network using longitudinal modes of an injection laser with external feedback

A new optical neural-network concept using the control of the modes of an injection laser by external feedback is described by a simple laser model. This approach uses the wavelength dispersed longitudinal modes of the laser as neurons and the amount of external feedback as connection weights. The predictions of the simple model are confirmed both with extensive numerical examples using the laser rate equations and also by experiments with GaAlAs injection lasers. The inputs and connection weights to this laser neural network are provided by external masks which control the amount of feedback reaching the laser. Stochastic learning is used to obtain weight masks for a small three-input and four-output neural net for the numerical and experimental examples. Winner-take-all and exclusive-or operations are obtained on the input set with different weight masks. Both of these operations are also obtained in experiments with a three-input/four-output laser neural network operating at an estimated speed greater than 10 GCPS. The eventual speed of this type of neural network hardware is expected to reach well within TCPS range if it is built in an optoelectronic integrated circuit with dimensions in the order of a mm. Different neural-network architectures possible with this approach are discussed.

An injection laser neural network

In this study an optical laser neural network is described, using longitudinal modes of an injection laser as individual neurons. Neural action is provided by external optical feedback into the active layer of the laser. Sigmoidal thresholding of weighted inputs is governed by injection laser physics under conditions of optical feedback. Training of the network is done by supervised stochastic learning algorithms. Ultimate network response times are estimated to exceed the 10 giga connections per second (GCPS) range.